From 110f3a34f1f36d0ea999d4aa34bbe66d5f2a01da Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Sun, 16 Dec 2018 15:02:59 +0100
Subject: skip empty, pull citations again

---
 scraper/README.md                                        |  4 ++--
 scraper/pdf_dump_first_page.sh                           |  8 +++++++-
 scraper/reports/report_coverage.html                     |  2 +-
 scraper/reports/report_index.html                        |  2 +-
 scraper/s2-citation-report.py                            |  8 ++++++--
 scraper/s2-geocode-spreadsheet.py                        |  2 +-
 scraper/util.py                                          | 16 ++++++++++------
 .../013909077ad843eb6df7a3e8e290cfd5575999d2.json        |  1 +
 .../044d9a8c61383312cdafbcc44b9d00d650b21c70.json        |  1 +
 .../066d71fcd997033dce4ca58df924397dfe0b5fd1.json        |  1 +
 .../08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.json        |  1 +
 .../0f0fcf041559703998abf310e56f8a2f90ee6f21.json        |  1 +
 .../18c72175ddbb7d5956d180b65a96005c100f6014.json        |  1 +
 .../27a2fad58dd8727e280f97036e0d2bc55ef5424c.json        |  2 +-
 .../28312c3a47c1be3a67365700744d3d6665b86f22.json        |  1 +
 .../28d4e027c7e90b51b7d8908fce68128d1964668a.json        |  1 +
 .../2a171f8d14b6b8735001a11c217af9587d095848.json        |  2 +-
 .../2ce2560cf59db59ce313bbeb004e8ce55c5ce928.json        |  1 +
 .../2d3482dcff69c7417c7b933f22de606a0e8e42d4.json        |  1 +
 .../2edb87494278ad11641b6cf7a3f8996de12b8e14.json        |  1 +
 .../2f43b614607163abf41dfe5d17ef6749a1b61304.json        |  1 +
 .../370b5757a5379b15e30d619e4d3fb9e8e13f3256.json        |  2 +-
 .../447d8893a4bdc29fa1214e53499ffe67b28a6db5.json        |  1 +
 .../46a01565e6afe7c074affb752e7069ee3bf2e4ef.json        |  1 +
 .../4793f11fbca4a7dba898b9fff68f70d868e2497c.json        |  1 +
 .../4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.json        |  1 +
 .../670637d0303a863c1548d5b19f705860a23e285c.json        |  1 +
 .../6dd0597f8513dc100cd0bc1b493768cde45098a9.json        |  2 +-
 .../7f4040b482d16354d5938c1d1b926b544652bf5b.json        |  1 +
 .../833fa04463d90aab4a9fe2870d480f0b40df446e.json        |  1 +
 .../8355d095d3534ef511a9af68a3b2893339e3f96b.json        |  1 +
 .../98bb029afe2a1239c3fdab517323066f0957b81b.json        |  1 +
 .../a0fd85b3400c7b3e11122f44dc5870ae2de9009a.json        |  1 +
 .../b1f4423c227fa37b9680787be38857069247a307.json        |  1 +
 .../bd88bb2e4f351352d88ee7375af834360e223498.json        |  1 +
 .../c570d1247e337f91e555c3be0e8c8a5aba539d9f.json        |  1 +
 .../f72f6a45ee240cc99296a287ff725aaa7e7ebb35.json        |  1 +
 .../fcc6fe6007c322641796cb8792718641856a22a7.json        |  2 +-
 38 files changed, 59 insertions(+), 19 deletions(-)
 create mode 100644 site/datasets/citations/013909077ad843eb6df7a3e8e290cfd5575999d2.json
 create mode 100644 site/datasets/citations/044d9a8c61383312cdafbcc44b9d00d650b21c70.json
 create mode 100644 site/datasets/citations/066d71fcd997033dce4ca58df924397dfe0b5fd1.json
 create mode 100644 site/datasets/citations/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.json
 create mode 100644 site/datasets/citations/0f0fcf041559703998abf310e56f8a2f90ee6f21.json
 create mode 100644 site/datasets/citations/18c72175ddbb7d5956d180b65a96005c100f6014.json
 create mode 100644 site/datasets/citations/28312c3a47c1be3a67365700744d3d6665b86f22.json
 create mode 100644 site/datasets/citations/28d4e027c7e90b51b7d8908fce68128d1964668a.json
 create mode 100644 site/datasets/citations/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.json
 create mode 100644 site/datasets/citations/2d3482dcff69c7417c7b933f22de606a0e8e42d4.json
 create mode 100644 site/datasets/citations/2edb87494278ad11641b6cf7a3f8996de12b8e14.json
 create mode 100644 site/datasets/citations/2f43b614607163abf41dfe5d17ef6749a1b61304.json
 create mode 100644 site/datasets/citations/447d8893a4bdc29fa1214e53499ffe67b28a6db5.json
 create mode 100644 site/datasets/citations/46a01565e6afe7c074affb752e7069ee3bf2e4ef.json
 create mode 100644 site/datasets/citations/4793f11fbca4a7dba898b9fff68f70d868e2497c.json
 create mode 100644 site/datasets/citations/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.json
 create mode 100644 site/datasets/citations/670637d0303a863c1548d5b19f705860a23e285c.json
 create mode 100644 site/datasets/citations/7f4040b482d16354d5938c1d1b926b544652bf5b.json
 create mode 100644 site/datasets/citations/833fa04463d90aab4a9fe2870d480f0b40df446e.json
 create mode 100644 site/datasets/citations/8355d095d3534ef511a9af68a3b2893339e3f96b.json
 create mode 100644 site/datasets/citations/98bb029afe2a1239c3fdab517323066f0957b81b.json
 create mode 100644 site/datasets/citations/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.json
 create mode 100644 site/datasets/citations/b1f4423c227fa37b9680787be38857069247a307.json
 create mode 100644 site/datasets/citations/bd88bb2e4f351352d88ee7375af834360e223498.json
 create mode 100644 site/datasets/citations/c570d1247e337f91e555c3be0e8c8a5aba539d9f.json
 create mode 100644 site/datasets/citations/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.json

diff --git a/scraper/README.md b/scraper/README.md
index 318bba9a..4399abd3 100644
--- a/scraper/README.md
+++ b/scraper/README.md
@@ -70,9 +70,9 @@ Included in the content-script folder is a Chrome extension which scrapes Google
 
 Once you have the data from S2, you can scrape all the PDFs (and other URLs) you find, and then extract institutions from those and geocode them.
 
-### s2-dump-pdf-urls.py
+### s2-dump-db-pdf-urls.py
 
-Dump PDF urls (and also IEEE urls etc) to CSV files.
+Dump PDF urls (and also DOI urls etc) to CSV files.
 
 ### s2-fetch-pdf.py
 
diff --git a/scraper/pdf_dump_first_page.sh b/scraper/pdf_dump_first_page.sh
index 2749915d..6277d40c 100644
--- a/scraper/pdf_dump_first_page.sh
+++ b/scraper/pdf_dump_first_page.sh
@@ -6,7 +6,13 @@ for i in datasets/s2/pdf/*/*/*.pdf
     if [[ ! -e $OUTPUT ]]
     then
       pdf2txt.py -p 1 $i > $OUTPUT
-      echo $OUTPUT
+      if [ -s $OUTPUT ]
+      then
+        echo "found $OUTPUT"
+      else
+        echo "rm empty $OUTPUT"
+        rm -f $OUTPUT
+      fi
     else
       if [ -s $OUTPUT ]
       then
diff --git a/scraper/reports/report_coverage.html b/scraper/reports/report_coverage.html
index 51e53e72..41716aaa 100644
--- a/scraper/reports/report_coverage.html
+++ b/scraper/reports/report_coverage.html
@@ -1 +1 @@
-<!doctype html><html><head><meta charset='utf-8'><title>Coverage</title><link rel='stylesheet' href='reports.css'></head><body><h2>Coverage</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1212.0402.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>University of Central Florida</td><td>28.59899755</td><td>-81.19712501</td><td>54%</td><td>999</td><td>535</td><td>464</td><td>73</td><td>708</td><td>212</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html">Face detection, pose estimation, and landmark localization in the wild</a></td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of California, Irvine</td><td>33.64319010</td><td>-117.84016494</td><td>52%</td><td>999</td><td>521</td><td>478</td><td>59</td><td>607</td><td>273</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>put_face</td><td>Put Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html">Attribute and simile classifiers for face verification</a></td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>51%</td><td>894</td><td>454</td><td>440</td><td>55</td><td>587</td><td>222</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td></td><td></td><td></td><td></td><td>41%</td><td>975</td><td>403</td><td>572</td><td>65</td><td>460</td><td>345</td></tr><tr><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td><td>a_pascal_yahoo</td><td>aPascal</td><td><a href="papers/2e384f057211426ac5922f1b33d2aa8df5d51f57.html">Describing objects by their attributes</a></td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>727</td><td>73</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html">Deep Face Recognition</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>621</td><td>156</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf">[pdf]</a></td><td></td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>38%</td><td>999</td><td>380</td><td>619</td><td>75</td><td>555</td><td>252</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html">VQA: Visual Question Answering</a></td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>47%</td><td>731</td><td>344</td><td>387</td><td>47</td><td>628</td><td>4</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html">Deep Learning Face Attributes in the Wild</a></td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>42%</td><td>808</td><td>340</td><td>468</td><td>69</td><td>666</td><td>50</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html">International Conference on Automatic Face and Gesture Recognition The CMU Pose , Illumination , and Expression ( PIE ) Database</a></td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>742</td><td>330</td><td>412</td><td>61</td><td>410</td><td>232</td></tr><tr><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td><td>voc</td><td>VOC</td><td><a href="papers/abe9f3b91fd26fa1b50cd685c0d20debfb372f73.html">The Pascal Visual Object Classes Challenge: A Retrospective</a></td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>999</td><td>315</td><td>684</td><td>75</td><td>698</td><td>6</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf">[pdf]</a></td><td></td><td>edu</td><td>Kyushu University</td><td>33.59914655</td><td>130.22359848</td><td>36%</td><td>848</td><td>308</td><td>540</td><td>56</td><td>413</td><td>255</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html">Nonparametric Object and Scene Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>999</td><td>305</td><td>694</td><td>93</td><td>670</td><td>9</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfw_p</td><td>LFWP</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>53%</td><td>521</td><td>274</td><td>247</td><td>40</td><td>321</td><td>144</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html">Overview of the face recognition grand challenge</a></td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>253</td><td>746</td><td>110</td><td>572</td><td>64</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html">Face recognition in unconstrained videos with matched background similarity</a></td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Open University of Israel</td><td>32.77824165</td><td>34.99565673</td><td>50%</td><td>485</td><td>244</td><td>240</td><td>32</td><td>290</td><td>140</td></tr><tr><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td><td>sheffield</td><td>Sheffield Face</td><td><a href="papers/3607afdb204de9a5a9300ae98aa4635d9effcda2.html">Face Description with Local Binary Patterns: Application to Face Recognition</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>999</td><td>238</td><td>761</td><td>65</td><td>483</td><td>87</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html">Learning Face Representation from Scratch</a></td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>53%</td><td>436</td><td>233</td><td>203</td><td>32</td><td>284</td><td>115</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>707</td><td>223</td><td>484</td><td>62</td><td>482</td><td>18</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html">Histograms of oriented gradients for human detection</a></td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>22%</td><td>999</td><td>217</td><td>782</td><td>67</td><td>520</td><td>22</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html">O M 4 . 1 The Subject Database 4 . 2 Experiment Plan 5 . 1 Varying the Overlap 4 Experimental Setup 5 Parameterisation Results</a></td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>999</td><td>214</td><td>785</td><td>96</td><td>550</td><td>57</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td><td>kdef</td><td>KDEF</td><td><a href="papers/93884e46c49f7ae1c7c34046fbc28882f2bd6341.html">Gaze fixation and the neural circuitry of face processing in autism</a></td><td><a href="{'url': 'http://doi.org/10.1038/nn1421', 'linkType': 'nature'}">[pdf]</a></td><td>Nature Neuroscience</td><td></td><td></td><td></td><td></td><td>31%</td><td>608</td><td>190</td><td>418</td><td>92</td><td>463</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk03</td><td>CUHK03</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>35%</td><td>512</td><td>180</td><td>332</td><td>29</td><td>323</td><td>4</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866', 'linkType': 'ieee'}">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>50%</td><td>356</td><td>179</td><td>177</td><td>21</td><td>299</td><td>3</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html">Interactive Facial Feature Localization</a></td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>52%</td><td>339</td><td>177</td><td>162</td><td>27</td><td>208</td><td>100</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html">Robust Face Landmark Estimation under Occlusion</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>55%</td><td>305</td><td>167</td><td>138</td><td>16</td><td>186</td><td>95</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>584</td><td>159</td><td>425</td><td>38</td><td>336</td><td>9</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>imm_face</td><td>IMM Face Dataset</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>muct</td><td>MUCT</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html">Scalable Person Re-identification: A Benchmark</a></td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>38%</td><td>394</td><td>149</td><td>245</td><td>18</td><td>271</td><td>3</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html">Web-based database for facial expression analysis</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td>32%</td><td>440</td><td>142</td><td>298</td><td>44</td><td>258</td><td>82</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td>41%</td><td>323</td><td>131</td><td>192</td><td>29</td><td>221</td><td>25</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html">A 3D facial expression database for facial behavior research</a></td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>24%</td><td>555</td><td>131</td><td>424</td><td>47</td><td>283</td><td>48</td></tr><tr><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td><td>coco</td><td>COCO</td><td><a href="papers/696ca58d93f6404fea0fc75c62d1d7b378f47628.html">Microsoft COCO Captions: Data Collection and Evaluation Server</a></td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>46%</td><td>283</td><td>129</td><td>154</td><td>16</td><td>231</td><td>4</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>498</td><td>127</td><td>371</td><td>55</td><td>319</td><td>32</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html">Presentation and validation of the Radboud Faces Database</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>28%</td><td>446</td><td>127</td><td>319</td><td>43</td><td>307</td><td>19</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces_news</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html">Names and faces in the news</a></td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td>41%</td><td>294</td><td>120</td><td>174</td><td>24</td><td>207</td><td>45</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>48%</td><td>222</td><td>107</td><td>115</td><td>21</td><td>158</td><td>48</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>berkeley_pose</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html">Describing people: A poselet-based approach to attribute classification</a></td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>43%</td><td>221</td><td>96</td><td>125</td><td>14</td><td>160</td><td>23</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html">Recognition using visual phrases</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>41%</td><td>233</td><td>95</td><td>138</td><td>18</td><td>174</td><td>5</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>352</td><td>94</td><td>258</td><td>26</td><td>195</td><td>3</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html">Exploring Models and Data for Image Question Answering</a></td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>191</td><td>83</td><td>108</td><td>12</td><td>163</td><td>1</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>167</td><td>83</td><td>84</td><td>15</td><td>131</td><td>27</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html">Depth and Appearance for Mobile Scene Analysis</a></td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>25%</td><td>319</td><td>79</td><td>240</td><td>27</td><td>192</td><td>0</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html">WIDER FACE: A Face Detection Benchmark</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.06523.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>53%</td><td>148</td><td>78</td><td>70</td><td>16</td><td>107</td><td>34</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>51%</td><td>148</td><td>76</td><td>72</td><td>8</td><td>109</td><td>3</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>m2vts</td><td>m2vts</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw_a</td><td>LFW-a</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>42%</td><td>177</td><td>75</td><td>102</td><td>15</td><td>102</td><td>54</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html">Age and Gender Estimation of Unfiltered Faces</a></td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td></td><td></td><td></td><td></td><td>43%</td><td>168</td><td>72</td><td>96</td><td>7</td><td>89</td><td>53</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html">Understanding images of groups of people</a></td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>36%</td><td>202</td><td>72</td><td>130</td><td>12</td><td>126</td><td>24</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html">Learning effective human pose estimation from inaccurate annotation</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>40%</td><td>173</td><td>70</td><td>103</td><td>9</td><td>116</td><td>2</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk01</td><td>CUHK01</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>258</td><td>67</td><td>191</td><td>12</td><td>141</td><td>1</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>towncenter</td><td>TownCenter</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html">Stable Multi-Target Tracking in Real-Time Surveillance Video (Preprint)</a></td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>310</td><td>64</td><td>246</td><td>24</td><td>177</td><td>4</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html">Bosphorus Database for 3D Face Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>328</td><td>58</td><td>270</td><td>18</td><td>143</td><td>37</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462', 'linkType': 'ieee'}">[pdf]</a></td><td>Face and Gesture 2011</td><td></td><td></td><td></td><td></td><td>32%</td><td>184</td><td>58</td><td>126</td><td>23</td><td>112</td><td>23</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk02</td><td>CUHK02</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html">Locally Aligned Feature Transforms across Views</a></td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>24%</td><td>242</td><td>57</td><td>185</td><td>17</td><td>139</td><td>1</td></tr><tr><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2f5d44dc3e1b5955942133ff872ebd31716ec604.html">2D and 3D face recognition: A survey</a></td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>15%</td><td>389</td><td>57</td><td>332</td><td>28</td><td>198</td><td>17</td></tr><tr><td>a6e695ddd07aad719001c0fc1129328452385949</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/a6e695ddd07aad719001c0fc1129328452385949.html">The New Data and New Challenges in Multimedia Research</a></td><td><span class="gray">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>36%</td><td>160</td><td>57</td><td>103</td><td>11</td><td>105</td><td>4</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html">A data-driven approach to cleaning large face datasets</a></td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>46%</td><td>123</td><td>56</td><td>67</td><td>6</td><td>95</td><td>21</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>36%</td><td>151</td><td>54</td><td>97</td><td>7</td><td>85</td><td>26</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html">On a large sequence-based human gait database</a></td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>148</td><td>54</td><td>94</td><td>16</td><td>98</td><td>0</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html">Appearance-based gaze estimation in the wild</a></td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Max Planck Institute for Informatics</td><td>49.25795660</td><td>7.04577417</td><td>38%</td><td>138</td><td>52</td><td>86</td><td>3</td><td>94</td><td>7</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>146</td><td>49</td><td>97</td><td>6</td><td>96</td><td>0</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf">[pdf]</a></td><td></td><td>edu</td><td>Tohoku University</td><td>38.25309450</td><td>140.87365930</td><td>53%</td><td>91</td><td>48</td><td>43</td><td>5</td><td>60</td><td>15</td></tr><tr><td>109df0e8e5969ddf01e073143e83599228a1163f</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/109df0e8e5969ddf01e073143e83599228a1163f.html">Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</a></td><td><a href="http://dl.acm.org/citation.cfm?id=2337184">[pdf]</a></td><td>2012 39th Annual International Symposium on Computer Architecture (ISCA)</td><td></td><td></td><td></td><td></td><td>25%</td><td>192</td><td>48</td><td>144</td><td>8</td><td>99</td><td>0</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>38%</td><td>125</td><td>48</td><td>77</td><td>6</td><td>72</td><td>1</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>University of Queensland</td><td>-27.49741805</td><td>153.01316956</td><td>30%</td><td>128</td><td>39</td><td>89</td><td>6</td><td>68</td><td>14</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html">Pedestrian Attribute Recognition At Far Distance</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>80</td><td>37</td><td>43</td><td>2</td><td>51</td><td>3</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html">Pruning training sets for learning of object categories</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308', 'linkType': 'ieee'}">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>58%</td><td>60</td><td>35</td><td>25</td><td>5</td><td>42</td><td>12</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/214c966d1f9c2a4b66f4535d9a0d4078e63a5867.html">Brainwash: A Data System for Feature Engineering</a></td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>57</td><td>34</td><td>23</td><td>2</td><td>50</td><td>0</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>77</td><td>34</td><td>43</td><td>4</td><td>57</td><td>1</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td>39%</td><td>83</td><td>32</td><td>51</td><td>2</td><td>62</td><td>3</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>72</td><td>32</td><td>40</td><td>7</td><td>54</td><td>8</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html">High Five: Recognising human interactions in TV shows</a></td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>91</td><td>31</td><td>60</td><td>11</td><td>64</td><td>1</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>68</td><td>30</td><td>38</td><td>8</td><td>49</td><td>7</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>60%</td><td>50</td><td>30</td><td>19</td><td>2</td><td>40</td><td>4</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>60%</td><td>48</td><td>29</td><td>19</td><td>2</td><td>45</td><td>2</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>47%</td><td>60</td><td>28</td><td>32</td><td>0</td><td>36</td><td>6</td></tr><tr><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td><td>gavab_db</td><td>Gavab</td><td><a href="papers/42505464808dfb446f521fc6ff2cfeffd4d68ff1.html">Expression invariant 3D face recognition with a Morphable Model</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376', 'linkType': 'ieee'}">[pdf]</a></td><td>2008 8th IEEE International Conference on Automatic Face & Gesture Recognition</td><td></td><td></td><td></td><td></td><td>29%</td><td>94</td><td>27</td><td>67</td><td>10</td><td>57</td><td>5</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>18%</td><td>151</td><td>27</td><td>124</td><td>9</td><td>78</td><td>2</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>73</td><td>26</td><td>47</td><td>6</td><td>39</td><td>16</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html">Recognize complex events from static images by fusing deep channels</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>58%</td><td>45</td><td>26</td><td>19</td><td>1</td><td>30</td><td>12</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html">Person Re-identification in the Wild</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Technology Sydney</td><td>-33.88096510</td><td>151.20107299</td><td>38%</td><td>65</td><td>25</td><td>40</td><td>1</td><td>46</td><td>0</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html">Automatic 3D face authentication</a></td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>25%</td><td>95</td><td>24</td><td>71</td><td>8</td><td>60</td><td>2</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td>edu</td><td>Jacobs University</td><td>53.41291480</td><td>-2.96897915</td><td>44%</td><td>52</td><td>23</td><td>29</td><td>5</td><td>37</td><td>4</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html">Object Detection Combining Recognition and Segmentation</a></td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>23%</td><td>101</td><td>23</td><td>78</td><td>11</td><td>58</td><td>0</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><a href="http://doi.acm.org/10.1145/2072572.2072590">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>122</td><td>22</td><td>100</td><td>11</td><td>71</td><td>1</td></tr><tr><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/eb027969f9310e0ae941e2adee2d42cdf07d938c.html">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1710.08092.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>38%</td><td>56</td><td>21</td><td>35</td><td>6</td><td>50</td><td>3</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html">The MUG facial expression database</a></td><td><a href="http://ieeexplore.ieee.org/document/5617662/">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>40.62984145</td><td>22.95889350</td><td>28%</td><td>68</td><td>19</td><td>49</td><td>5</td><td>28</td><td>19</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html">The intrinsic memorability of face photographs.</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td>36%</td><td>47</td><td>17</td><td>30</td><td>3</td><td>33</td><td>1</td></tr><tr><td>69a68f9cf874c69e2232f47808016c2736b90c35</td><td>celeba_plus</td><td>CelebFaces+</td><td><a href="papers/69a68f9cf874c69e2232f47808016c2736b90c35.html">Learning Deep Representation for Imbalanced Classification</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>33%</td><td>51</td><td>17</td><td>34</td><td>1</td><td>39</td><td>2</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>39%</td><td>44</td><td>17</td><td>27</td><td>0</td><td>36</td><td>0</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html">Violent flows: Real-time detection of violent crowd behavior</a></td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>20%</td><td>83</td><td>17</td><td>66</td><td>6</td><td>42</td><td>2</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>CAFE</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>33</td><td>16</td><td>17</td><td>3</td><td>28</td><td>1</td></tr><tr><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/c34532fe6bfbd1e6df477c9ffdbb043b77e7804d.html">A 3D Morphable Eye Region Model for Gaze Estimation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>67%</td><td>24</td><td>16</td><td>8</td><td>0</td><td>18</td><td>4</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html">The CMU Face In Action (FIA) Database</a></td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>55</td><td>16</td><td>39</td><td>5</td><td>38</td><td>7</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="https://doi.org/10.1007/s11263-013-0672-6">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>1</td><td>18</td><td>1</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>20</td><td>0</td></tr><tr><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/a5a44a32a91474f00a3cda671a802e87c899fbb4.html">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1801.03150.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>25</td><td>0</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html">RONCHI AND PERONA: DESCRIBING COMMON HUMAN VISUAL ACTIONS IN IMAGES 1 Describing Common Human Visual Actions in Images</a></td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>26</td><td>14</td><td>12</td><td>0</td><td>25</td><td>0</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html">End-to-End Deep Learning for Person Search</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf', 'linkType': 's2'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>34%</td><td>41</td><td>14</td><td>27</td><td>2</td><td>27</td><td>0</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html">Personalizing Human Video Pose Estimation</a></td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>32</td><td>14</td><td>18</td><td>2</td><td>27</td><td>0</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html">UMB-DB: A database of partially occluded 3D faces</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>29%</td><td>45</td><td>13</td><td>32</td><td>2</td><td>20</td><td>3</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>31%</td><td>39</td><td>12</td><td>27</td><td>2</td><td>27</td><td>7</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td></td><td></td><td></td><td></td><td>16%</td><td>75</td><td>12</td><td>63</td><td>6</td><td>25</td><td>8</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html">Fine-grained evaluation on face detection in the wild</a></td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Chinese Academy of Sciences</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>13</td><td>4</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html">Consistent Re-identification in a Camera Network</a></td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>45</td><td>12</td><td>33</td><td>7</td><td>34</td><td>1</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td>21%</td><td>58</td><td>12</td><td>46</td><td>7</td><td>40</td><td>1</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>67%</td><td>18</td><td>12</td><td>6</td><td>0</td><td>16</td><td>0</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>0</td><td>18</td><td>0</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>25%</td><td>40</td><td>10</td><td>30</td><td>0</td><td>20</td><td>6</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>15</td><td>3</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>50_people_one_question</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>11</td><td>2</td></tr><tr><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td><td>agedb</td><td>AgeDB</td><td><a href="papers/6dcf418c778f528b5792104760f1fbfe90c6dd6a.html">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>82%</td><td>11</td><td>9</td><td>2</td><td>0</td><td>10</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html">Automated Human Identification Using Ear Imaging</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>13%</td><td>70</td><td>9</td><td>61</td><td>6</td><td>28</td><td>1</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>10</td><td>4</td></tr><tr><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td><td>nudedetection</td><td>Nude Detection</td><td><a href="papers/7ace44190729927e5cb0dd5d363fcae966fe13f7.html">A bag-of-features approach based on Hue-SIFT descriptor for nude detection</a></td><td><a href="http://ieeexplore.ieee.org/document/7077625/">[pdf]</a></td><td>2009 17th European Signal Processing Conference</td><td></td><td></td><td></td><td></td><td>18%</td><td>51</td><td>9</td><td>42</td><td>1</td><td>18</td><td>0</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>20</td><td>8</td><td>12</td><td>2</td><td>16</td><td>0</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928', 'linkType': 'ieee'}">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td></td><td></td><td></td><td></td><td>25%</td><td>32</td><td>8</td><td>24</td><td>3</td><td>16</td><td>1</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>47%</td><td>17</td><td>8</td><td>9</td><td>1</td><td>12</td><td>1</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html">Ear recognition: More than a survey</a></td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td>29%</td><td>24</td><td>7</td><td>17</td><td>0</td><td>11</td><td>0</td></tr><tr><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td><td>camel</td><td>CAMEL</td><td><a href="papers/5801690199c1917fa58c35c3dead177c0b8f9f2d.html">Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf">[pdf]</a></td><td>Remote Sensing</td><td></td><td></td><td></td><td></td><td>37%</td><td>19</td><td>7</td><td>12</td><td>1</td><td>16</td><td>0</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td></td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td>22%</td><td>32</td><td>7</td><td>25</td><td>2</td><td>17</td><td>0</td></tr><tr><td>ec792ad2433b6579f2566c932ee414111e194537</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/ec792ad2433b6579f2566c932ee414111e194537.html">Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1711.08565.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>50%</td><td>14</td><td>7</td><td>7</td><td>1</td><td>11</td><td>0</td></tr><tr><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td><td>pornodb</td><td>Pornography DB</td><td><a href="papers/b92a1ed9622b8268ae3ac9090e25789fc41cc9b8.html">Pooling in image representation: The visual codeword point of view</a></td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>9%</td><td>77</td><td>7</td><td>70</td><td>7</td><td>43</td><td>2</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html">Three-dimensional face recognition: an eigensurface approach</a></td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td>19%</td><td>36</td><td>7</td><td>29</td><td>4</td><td>25</td><td>1</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html">Recognizing disguised faces</a></td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>24</td><td>6</td><td>18</td><td>0</td><td>16</td><td>1</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html">IARPA Janus Benchmark-B Face Dataset</a></td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>24%</td><td>25</td><td>6</td><td>19</td><td>6</td><td>21</td><td>3</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.02459.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td>edu</td><td>South China University of Technology</td><td>23.05020420</td><td>113.39880323</td><td>43%</td><td>14</td><td>6</td><td>8</td><td>3</td><td>5</td><td>7</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>24%</td><td>21</td><td>5</td><td>16</td><td>3</td><td>11</td><td>1</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html">Re-identify people in wide area camera network</a></td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>9%</td><td>55</td><td>5</td><td>50</td><td>2</td><td>35</td><td>0</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1605.09653.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE transactions on pattern analysis and machine intelligence</td><td></td><td></td><td></td><td></td><td>27%</td><td>15</td><td>4</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td><td>buhmap_db</td><td>BUHMAP-DB </td><td><a href="papers/014b8df0180f33b9fea98f34ae611c6447d761d2.html">Facial feature tracking and expression recognition for sign language</a></td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf">[pdf]</a></td><td>2008 23rd International Symposium on Computer and Information Sciences</td><td></td><td></td><td></td><td></td><td>16%</td><td>25</td><td>4</td><td>21</td><td>1</td><td>10</td><td>2</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html">Adaptive Image Sampling and Windows Classification for On–board Pedestrian Detection</a></td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>44</td><td>4</td><td>40</td><td>1</td><td>21</td><td>0</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>14%</td><td>29</td><td>4</td><td>25</td><td>3</td><td>17</td><td>0</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Michigan State University</td><td>42.71856800</td><td>-84.47791571</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>5</td><td>0</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html">Faces in Places: compound query retrieval</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html">ChaLearn looking at people: A review of events and resources</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1701.02664.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td>30%</td><td>10</td><td>3</td><td>7</td><td>1</td><td>6</td><td>0</td></tr><tr><td>a5acda0e8c0937bfed013e6382da127103e41395</td><td>disfa</td><td>DISFA</td><td><a href="papers/a5acda0e8c0937bfed013e6382da127103e41395.html">Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>38%</td><td>8</td><td>3</td><td>5</td><td>1</td><td>5</td><td>0</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217', 'linkType': 'ieee'}">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td>33%</td><td>9</td><td>3</td><td>6</td><td>2</td><td>9</td><td>0</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html">Large scale unconstrained open set face database</a></td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of Colorado at Colorado Springs</td><td>38.89646790</td><td>-104.80505940</td><td>60%</td><td>5</td><td>3</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html">USED: a large-scale social event detection dataset</a></td><td><a href="http://doi.acm.org/10.1145/2910017.2910624">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html">VADANA: A dense dataset for facial image analysis</a></td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>19%</td><td>16</td><td>3</td><td>13</td><td>0</td><td>6</td><td>6</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf">[pdf]</a></td><td></td><td>edu</td><td>California Institute of Technology</td><td>34.13710185</td><td>-118.12527487</td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>6</td><td>1</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html">Visual Kinship Recognition of Families in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>67%</td><td>3</td><td>2</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/6dbe8e5121c534339d6e41f8683e85f87e6abf81.html">Clothing Cosegmentation for Shopping Images With Cluttered Background</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>33%</td><td>6</td><td>2</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td><td>ilids_vid_reid</td><td>iLIDS-VID</td><td><a href="papers/99eb4cea0d9bc9fe777a5c5172f8638a37a7f262.html">Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</a></td><td><a href="https://doi.org/10.1109/LSP.2016.2574323">[pdf]</a></td><td>IEEE Signal Processing Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>4</td><td>0</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html">Large Age-Gap face verification by feature injection in deep networks</a></td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>2</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market1203</td><td>Market 1203</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html">Investigating Open-World Person Re-identi cation Using a Drone</a></td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>5</td><td>2</td><td>3</td><td>0</td><td>3</td><td>0</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html">How to Take a Good Selfie?</a></td><td><a href="http://doi.acm.org/10.1145/2733373.2806365">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>czech_news_agency</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>10%</td><td>10</td><td>1</td><td>9</td><td>0</td><td>4</td><td>2</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html">A Multi-modal Graphical Model for Scene Analysis</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td>12%</td><td>8</td><td>1</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td><td>face_research_lab</td><td>Face Research Lab London</td><td><a href="papers/c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8.html">Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</a></td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf">[pdf]</a></td><td>Scientific reports</td><td>edu</td><td>University College London</td><td>51.52316070</td><td>-0.12820370</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><a href="http://doi.acm.org/10.1145/2676440.2676443">[pdf]</a></td><td></td><td>edu</td><td>University of Kentucky</td><td>38.03337420</td><td>-84.50177580</td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html">Indian Face Age Database : A Database for Face Recognition with Age Variation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td><td>megaage</td><td>MegaAge</td><td><a href="papers/d80a3d1f3a438e02a6685e66ee908446766fefa9.html">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1708.09687.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>1</td><td>4</td><td>0</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html">Spoofing faces using makeup: An investigative study</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>43.61581310</td><td>7.06838000</td><td>20%</td><td>5</td><td>1</td><td>4</td><td>0</td><td>1</td><td>2</td></tr><tr><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/fb82681ac5d3487bd8e52dbb3d1fa220eeac855e.html">1 Network Notebook</a></td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>11</td><td>1</td><td>10</td><td>1</td><td>10</td><td>0</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1703.06283.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>8%</td><td>12</td><td>1</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html">ReSEED: social event dEtection dataset</a></td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>17%</td><td>6</td><td>1</td><td>5</td><td>1</td><td>1</td><td>1</td></tr><tr><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709.html">Learning to predict human behaviour in crowded scenes</a></td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>20%</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>0</td></tr><tr><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/9696ad8b164f5e10fcfe23aacf74bd6168aebb15.html">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1712.01443.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>4</td><td>0</td><td>4</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/f152b6ee251cca940dd853c54e6a7b78fbc6b235.html">Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</a></td><td><a href="{'url': 'http://dl.acm.org/citation.cfm?id=3232665', 'linkType': 'acm'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td><td>bjut_3d</td><td>BJUT-3D</td><td><a href="papers/1ed1a49534ad8dd00f81939449f6389cfbc25321.html">A novel face recognition method based on 3D face model</a></td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202">[pdf]</a></td><td>2007 IEEE International Conference on Robotics and Biomimetics (ROBIO)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>1</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4.html">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1805.07193.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html">FDDB: A Benchmark for Face Detection in Unconstrained Settings</a></td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td><td>fei</td><td>FEI</td><td><a href="papers/b6b1b0632eb9d4ab1427278f5e5c46f97753c73d.html">Generalização cartográfica automatizada para um banco de dados cadastral</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd.html">High-resolution comprehensive 3-D dynamic database for facial articulation analysis</a></td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>5</td><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td></tr><tr><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/ad62c6e17bc39b4dec20d32f6ac667ae42d2c118.html">A Synchronization Ground Truth for the Jiku Mobile Video Dataset</a></td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html">Component-Based Face Recognition with 3D Morphable Models</a></td><td><a href="http://www.bheisele.com/avbpa2003.pdf">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td>0%</td><td>12</td><td>0</td><td>12</td><td>0</td><td>8</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html">PETS 2017: Dataset and Challenge</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>8</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku</td><td>PKU</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>1</td><td>0</td></tr><tr><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/c866a2afc871910e3282fd9498dce4ab20f6a332.html">Surveillance Face Recognition Challenge</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.09691.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td><td>scface</td><td>SCface</td><td><a href="papers/f3b84a03985de3890b400b68e2a92c0a00afd9d0.html">Large Variability Surveillance Camera Face Database</a></td><td><span class="gray">[pdf]</a></td><td>2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim)</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.04326.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>377f2b65e6a9300448bdccf678cde59449ecd337</td><td>ufdd</td><td>UFDD</td><td><a href="papers/377f2b65e6a9300448bdccf678cde59449ecd337.html">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.10275.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Johns Hopkins University</td><td>39.32905300</td><td>-76.61942500</td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html">Re-identification of pedestrians with variable occlusion and scale</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>10</td><td>0</td><td>10</td><td>2</td><td>6</td><td>0</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><a href="http://doi.acm.org/10.1145/2996913.2996997">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>77c81c13a110a341c140995bedb98101b9e84f7f</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/77c81c13a110a341c140995bedb98101b9e84f7f.html">WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td></td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html">WLFDB: Weakly Labeled Face Databases</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html">YawDD: a yawning detection dataset</a></td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>14</td><td>0</td><td>14</td><td>1</td><td>2</td><td>1</td></tr></table></body></html>
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+<!doctype html><html><head><meta charset='utf-8'><title>Coverage</title><link rel='stylesheet' href='reports.css'></head><body><h2>Coverage</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1212.0402.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>University of Central Florida</td><td>28.59899755</td><td>-81.19712501</td><td>54%</td><td>999</td><td>535</td><td>464</td><td>73</td><td>708</td><td>212</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html">Face detection, pose estimation, and landmark localization in the wild</a></td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of California, Irvine</td><td>33.64319010</td><td>-117.84016494</td><td>52%</td><td>999</td><td>521</td><td>478</td><td>59</td><td>607</td><td>273</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>put_face</td><td>Put Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html">Attribute and simile classifiers for face verification</a></td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>51%</td><td>894</td><td>454</td><td>440</td><td>55</td><td>587</td><td>222</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td>42%</td><td>999</td><td>422</td><td>577</td><td>80</td><td>538</td><td>295</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td></td><td></td><td></td><td></td><td>41%</td><td>975</td><td>403</td><td>572</td><td>65</td><td>460</td><td>345</td></tr><tr><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td><td>a_pascal_yahoo</td><td>aPascal</td><td><a href="papers/2e384f057211426ac5922f1b33d2aa8df5d51f57.html">Describing objects by their attributes</a></td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>727</td><td>73</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html">Deep Face Recognition</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>621</td><td>156</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf">[pdf]</a></td><td></td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>38%</td><td>999</td><td>380</td><td>619</td><td>75</td><td>555</td><td>252</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html">VQA: Visual Question Answering</a></td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>47%</td><td>731</td><td>344</td><td>387</td><td>47</td><td>628</td><td>4</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html">Deep Learning Face Attributes in the Wild</a></td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>42%</td><td>808</td><td>340</td><td>468</td><td>69</td><td>666</td><td>50</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html">International Conference on Automatic Face and Gesture Recognition The CMU Pose , Illumination , and Expression ( PIE ) Database</a></td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>742</td><td>330</td><td>412</td><td>61</td><td>410</td><td>232</td></tr><tr><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td><td>voc</td><td>VOC</td><td><a href="papers/abe9f3b91fd26fa1b50cd685c0d20debfb372f73.html">The Pascal Visual Object Classes Challenge: A Retrospective</a></td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>999</td><td>315</td><td>684</td><td>75</td><td>698</td><td>6</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf">[pdf]</a></td><td></td><td>edu</td><td>Kyushu University</td><td>33.59914655</td><td>130.22359848</td><td>36%</td><td>848</td><td>308</td><td>540</td><td>56</td><td>413</td><td>255</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html">Nonparametric Object and Scene Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>999</td><td>305</td><td>694</td><td>93</td><td>670</td><td>9</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfw_p</td><td>LFWP</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>53%</td><td>521</td><td>274</td><td>247</td><td>40</td><td>321</td><td>144</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html">Overview of the face recognition grand challenge</a></td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>253</td><td>746</td><td>110</td><td>572</td><td>64</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html">Face recognition in unconstrained videos with matched background similarity</a></td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Open University of Israel</td><td>32.77824165</td><td>34.99565673</td><td>50%</td><td>485</td><td>244</td><td>240</td><td>32</td><td>290</td><td>140</td></tr><tr><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td><td>sheffield</td><td>Sheffield Face</td><td><a href="papers/3607afdb204de9a5a9300ae98aa4635d9effcda2.html">Face Description with Local Binary Patterns: Application to Face Recognition</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>999</td><td>238</td><td>761</td><td>65</td><td>483</td><td>87</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html">Learning Face Representation from Scratch</a></td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>53%</td><td>436</td><td>233</td><td>203</td><td>32</td><td>284</td><td>115</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>707</td><td>223</td><td>484</td><td>62</td><td>482</td><td>18</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html">Histograms of oriented gradients for human detection</a></td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>22%</td><td>999</td><td>217</td><td>782</td><td>67</td><td>520</td><td>22</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html">O M 4 . 1 The Subject Database 4 . 2 Experiment Plan 5 . 1 Varying the Overlap 4 Experimental Setup 5 Parameterisation Results</a></td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>999</td><td>214</td><td>785</td><td>96</td><td>550</td><td>57</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td><td>kdef</td><td>KDEF</td><td><a href="papers/93884e46c49f7ae1c7c34046fbc28882f2bd6341.html">Gaze fixation and the neural circuitry of face processing in autism</a></td><td><a href="{'url': 'http://doi.org/10.1038/nn1421', 'linkType': 'nature'}">[pdf]</a></td><td>Nature Neuroscience</td><td></td><td></td><td></td><td></td><td>31%</td><td>608</td><td>190</td><td>418</td><td>92</td><td>463</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk03</td><td>CUHK03</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>35%</td><td>512</td><td>180</td><td>332</td><td>29</td><td>323</td><td>4</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866', 'linkType': 'ieee'}">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>50%</td><td>356</td><td>179</td><td>177</td><td>21</td><td>299</td><td>3</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html">Interactive Facial Feature Localization</a></td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>52%</td><td>339</td><td>177</td><td>162</td><td>27</td><td>208</td><td>100</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html">Robust Face Landmark Estimation under Occlusion</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>55%</td><td>305</td><td>167</td><td>138</td><td>16</td><td>186</td><td>95</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td>edu</td><td>University of Twente</td><td>52.23801390</td><td>6.85667610</td><td>56%</td><td>285</td><td>159</td><td>125</td><td>28</td><td>188</td><td>82</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>584</td><td>159</td><td>425</td><td>38</td><td>336</td><td>9</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>imm_face</td><td>IMM Face Dataset</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>muct</td><td>MUCT</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html">Scalable Person Re-identification: A Benchmark</a></td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>38%</td><td>394</td><td>149</td><td>245</td><td>18</td><td>271</td><td>3</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html">Web-based database for facial expression analysis</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td>32%</td><td>440</td><td>142</td><td>298</td><td>44</td><td>258</td><td>82</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td>41%</td><td>323</td><td>131</td><td>192</td><td>29</td><td>221</td><td>25</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html">A 3D facial expression database for facial behavior research</a></td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>24%</td><td>555</td><td>131</td><td>424</td><td>47</td><td>283</td><td>48</td></tr><tr><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td><td>coco</td><td>COCO</td><td><a href="papers/696ca58d93f6404fea0fc75c62d1d7b378f47628.html">Microsoft COCO Captions: Data Collection and Evaluation Server</a></td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>46%</td><td>283</td><td>129</td><td>154</td><td>16</td><td>231</td><td>4</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>498</td><td>127</td><td>371</td><td>55</td><td>319</td><td>32</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html">Presentation and validation of the Radboud Faces Database</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>28%</td><td>446</td><td>127</td><td>319</td><td>43</td><td>307</td><td>19</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces_news</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html">Names and faces in the news</a></td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td>41%</td><td>294</td><td>120</td><td>174</td><td>24</td><td>207</td><td>45</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td>11%</td><td>999</td><td>109</td><td>259</td><td>32</td><td>213</td><td>51</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>48%</td><td>222</td><td>107</td><td>115</td><td>21</td><td>158</td><td>48</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>28312c3a47c1be3a67365700744d3d6665b86f22</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/28312c3a47c1be3a67365700744d3d6665b86f22.html">Face Recognition: A Literature Survey1</a></td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>11%</td><td>999</td><td>105</td><td>265</td><td>32</td><td>217</td><td>39</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Twente</td><td>52.23801390</td><td>6.85667610</td><td>59%</td><td>169</td><td>100</td><td>69</td><td>14</td><td>112</td><td>49</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>berkeley_pose</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html">Describing people: A poselet-based approach to attribute classification</a></td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>43%</td><td>221</td><td>96</td><td>125</td><td>14</td><td>160</td><td>23</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>10%</td><td>999</td><td>96</td><td>355</td><td>26</td><td>252</td><td>5</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html">Recognition using visual phrases</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>41%</td><td>233</td><td>95</td><td>138</td><td>18</td><td>174</td><td>5</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>352</td><td>94</td><td>258</td><td>26</td><td>195</td><td>3</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>32%</td><td>260</td><td>84</td><td>85</td><td>18</td><td>143</td><td>10</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html">Exploring Models and Data for Image Question Answering</a></td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>191</td><td>83</td><td>108</td><td>12</td><td>163</td><td>1</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>167</td><td>83</td><td>84</td><td>15</td><td>131</td><td>27</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html">Depth and Appearance for Mobile Scene Analysis</a></td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>25%</td><td>319</td><td>79</td><td>240</td><td>27</td><td>192</td><td>0</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html">WIDER FACE: A Face Detection Benchmark</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.06523.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>53%</td><td>148</td><td>78</td><td>70</td><td>16</td><td>107</td><td>34</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>51%</td><td>148</td><td>76</td><td>72</td><td>8</td><td>109</td><td>3</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>m2vts</td><td>m2vts</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw_a</td><td>LFW-a</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>42%</td><td>177</td><td>75</td><td>102</td><td>15</td><td>102</td><td>54</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html">Age and Gender Estimation of Unfiltered Faces</a></td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td></td><td></td><td></td><td></td><td>43%</td><td>168</td><td>72</td><td>96</td><td>7</td><td>89</td><td>53</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html">Understanding images of groups of people</a></td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>36%</td><td>202</td><td>72</td><td>130</td><td>12</td><td>126</td><td>24</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html">Learning effective human pose estimation from inaccurate annotation</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>40%</td><td>173</td><td>70</td><td>103</td><td>9</td><td>116</td><td>2</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk01</td><td>CUHK01</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>258</td><td>67</td><td>191</td><td>12</td><td>141</td><td>1</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>towncenter</td><td>TownCenter</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html">Stable Multi-Target Tracking in Real-Time Surveillance Video (Preprint)</a></td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>310</td><td>64</td><td>246</td><td>24</td><td>177</td><td>4</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>-35.27769990</td><td>149.11852700</td><td>33%</td><td>175</td><td>58</td><td>66</td><td>7</td><td>54</td><td>49</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html">Bosphorus Database for 3D Face Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>328</td><td>58</td><td>270</td><td>18</td><td>143</td><td>37</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462', 'linkType': 'ieee'}">[pdf]</a></td><td>Face and Gesture 2011</td><td></td><td></td><td></td><td></td><td>32%</td><td>184</td><td>58</td><td>126</td><td>23</td><td>112</td><td>23</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk02</td><td>CUHK02</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html">Locally Aligned Feature Transforms across Views</a></td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>24%</td><td>242</td><td>57</td><td>185</td><td>17</td><td>139</td><td>1</td></tr><tr><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2f5d44dc3e1b5955942133ff872ebd31716ec604.html">2D and 3D face recognition: A survey</a></td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>15%</td><td>389</td><td>57</td><td>332</td><td>28</td><td>198</td><td>17</td></tr><tr><td>a6e695ddd07aad719001c0fc1129328452385949</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/a6e695ddd07aad719001c0fc1129328452385949.html">The New Data and New Challenges in Multimedia Research</a></td><td><span class="gray">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>36%</td><td>160</td><td>57</td><td>103</td><td>11</td><td>105</td><td>4</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html">A data-driven approach to cleaning large face datasets</a></td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>46%</td><td>123</td><td>56</td><td>67</td><td>6</td><td>95</td><td>21</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>36%</td><td>151</td><td>54</td><td>97</td><td>7</td><td>85</td><td>26</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html">On a large sequence-based human gait database</a></td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>148</td><td>54</td><td>94</td><td>16</td><td>98</td><td>0</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html">Appearance-based gaze estimation in the wild</a></td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Max Planck Institute for Informatics</td><td>49.25795660</td><td>7.04577417</td><td>38%</td><td>138</td><td>52</td><td>86</td><td>3</td><td>94</td><td>7</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>146</td><td>49</td><td>97</td><td>6</td><td>96</td><td>0</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html">Person Re-identification by Video Ranking</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>25%</td><td>196</td><td>49</td><td>124</td><td>11</td><td>98</td><td>1</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf">[pdf]</a></td><td></td><td>edu</td><td>Tohoku University</td><td>38.25309450</td><td>140.87365930</td><td>53%</td><td>91</td><td>48</td><td>43</td><td>5</td><td>60</td><td>15</td></tr><tr><td>109df0e8e5969ddf01e073143e83599228a1163f</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/109df0e8e5969ddf01e073143e83599228a1163f.html">Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</a></td><td><a href="http://dl.acm.org/citation.cfm?id=2337184">[pdf]</a></td><td>2012 39th Annual International Symposium on Computer Architecture (ISCA)</td><td></td><td></td><td></td><td></td><td>25%</td><td>192</td><td>48</td><td>144</td><td>8</td><td>99</td><td>0</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>38%</td><td>125</td><td>48</td><td>77</td><td>6</td><td>72</td><td>1</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>41%</td><td>116</td><td>47</td><td>46</td><td>4</td><td>62</td><td>19</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390', 'linkType': 'ieee'}">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td>44%</td><td>102</td><td>45</td><td>54</td><td>6</td><td>62</td><td>28</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/dd1d/51c3a59cb71cbfe1433ebeb4d973f7f9ddc1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>193</td><td>42</td><td>133</td><td>15</td><td>101</td><td>2</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>University of Queensland</td><td>-27.49741805</td><td>153.01316956</td><td>30%</td><td>128</td><td>39</td><td>89</td><td>6</td><td>68</td><td>14</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html">Pedestrian Attribute Recognition At Far Distance</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>80</td><td>37</td><td>43</td><td>2</td><td>51</td><td>3</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html">Pruning training sets for learning of object categories</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308', 'linkType': 'ieee'}">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>58%</td><td>60</td><td>35</td><td>25</td><td>5</td><td>42</td><td>12</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1408.3967.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>33%</td><td>105</td><td>35</td><td>50</td><td>8</td><td>55</td><td>16</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1408.3967.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>33%</td><td>105</td><td>35</td><td>50</td><td>8</td><td>55</td><td>16</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/214c966d1f9c2a4b66f4535d9a0d4078e63a5867.html">Brainwash: A Data System for Feature Engineering</a></td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>57</td><td>34</td><td>23</td><td>2</td><td>50</td><td>0</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>77</td><td>34</td><td>43</td><td>4</td><td>57</td><td>1</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td>39%</td><td>83</td><td>32</td><td>51</td><td>2</td><td>62</td><td>3</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>72</td><td>32</td><td>40</td><td>7</td><td>54</td><td>8</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html">High Five: Recognising human interactions in TV shows</a></td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>91</td><td>31</td><td>60</td><td>11</td><td>64</td><td>1</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>68</td><td>30</td><td>38</td><td>8</td><td>49</td><td>7</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>60%</td><td>50</td><td>30</td><td>19</td><td>2</td><td>40</td><td>4</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>60%</td><td>48</td><td>29</td><td>19</td><td>2</td><td>45</td><td>2</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>47%</td><td>60</td><td>28</td><td>32</td><td>0</td><td>36</td><td>6</td></tr><tr><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td><td>gavab_db</td><td>Gavab</td><td><a href="papers/42505464808dfb446f521fc6ff2cfeffd4d68ff1.html">Expression invariant 3D face recognition with a Morphable Model</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376', 'linkType': 'ieee'}">[pdf]</a></td><td>2008 8th IEEE International Conference on Automatic Face & Gesture Recognition</td><td></td><td></td><td></td><td></td><td>29%</td><td>94</td><td>27</td><td>67</td><td>10</td><td>57</td><td>5</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>18%</td><td>151</td><td>27</td><td>124</td><td>9</td><td>78</td><td>2</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>73</td><td>26</td><td>47</td><td>6</td><td>39</td><td>16</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html">Recognize complex events from static images by fusing deep channels</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>58%</td><td>45</td><td>26</td><td>19</td><td>1</td><td>30</td><td>12</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html">Person Re-identification in the Wild</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Technology Sydney</td><td>-33.88096510</td><td>151.20107299</td><td>38%</td><td>65</td><td>25</td><td>40</td><td>1</td><td>46</td><td>0</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html">Automatic 3D face authentication</a></td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>25%</td><td>95</td><td>24</td><td>71</td><td>8</td><td>60</td><td>2</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td>edu</td><td>Jacobs University</td><td>53.41291480</td><td>-2.96897915</td><td>44%</td><td>52</td><td>23</td><td>29</td><td>5</td><td>37</td><td>4</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html">Object Detection Combining Recognition and Segmentation</a></td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>23%</td><td>101</td><td>23</td><td>78</td><td>11</td><td>58</td><td>0</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><a href="http://doi.acm.org/10.1145/2072572.2072590">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>122</td><td>22</td><td>100</td><td>11</td><td>71</td><td>1</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html">Understanding Kin Relationships in a Photo</a></td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>24%</td><td>89</td><td>21</td><td>46</td><td>2</td><td>20</td><td>14</td></tr><tr><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/eb027969f9310e0ae941e2adee2d42cdf07d938c.html">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1710.08092.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>38%</td><td>56</td><td>21</td><td>35</td><td>6</td><td>50</td><td>3</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html">The MUG facial expression database</a></td><td><a href="http://ieeexplore.ieee.org/document/5617662/">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>40.62984145</td><td>22.95889350</td><td>28%</td><td>68</td><td>19</td><td>49</td><td>5</td><td>28</td><td>19</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>22%</td><td>88</td><td>19</td><td>43</td><td>4</td><td>42</td><td>2</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html">The intrinsic memorability of face photographs.</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td>36%</td><td>47</td><td>17</td><td>30</td><td>3</td><td>33</td><td>1</td></tr><tr><td>69a68f9cf874c69e2232f47808016c2736b90c35</td><td>celeba_plus</td><td>CelebFaces+</td><td><a href="papers/69a68f9cf874c69e2232f47808016c2736b90c35.html">Learning Deep Representation for Imbalanced Classification</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>33%</td><td>51</td><td>17</td><td>34</td><td>1</td><td>39</td><td>2</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>39%</td><td>44</td><td>17</td><td>27</td><td>0</td><td>36</td><td>0</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html">Violent flows: Real-time detection of violent crowd behavior</a></td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>20%</td><td>83</td><td>17</td><td>66</td><td>6</td><td>42</td><td>2</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>CAFE</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>33</td><td>16</td><td>17</td><td>3</td><td>28</td><td>1</td></tr><tr><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/c34532fe6bfbd1e6df477c9ffdbb043b77e7804d.html">A 3D Morphable Eye Region Model for Gaze Estimation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>67%</td><td>24</td><td>16</td><td>8</td><td>0</td><td>18</td><td>4</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html">The CMU Face In Action (FIA) Database</a></td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>55</td><td>16</td><td>39</td><td>5</td><td>38</td><td>7</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="https://doi.org/10.1007/s11263-013-0672-6">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>1</td><td>18</td><td>1</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>20</td><td>0</td></tr><tr><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/a5a44a32a91474f00a3cda671a802e87c899fbb4.html">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1801.03150.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>25</td><td>0</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html">RONCHI AND PERONA: DESCRIBING COMMON HUMAN VISUAL ACTIONS IN IMAGES 1 Describing Common Human Visual Actions in Images</a></td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>26</td><td>14</td><td>12</td><td>0</td><td>25</td><td>0</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html">End-to-End Deep Learning for Person Search</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf', 'linkType': 's2'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>34%</td><td>41</td><td>14</td><td>27</td><td>2</td><td>27</td><td>0</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html">Personalizing Human Video Pose Estimation</a></td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>32</td><td>14</td><td>18</td><td>2</td><td>27</td><td>0</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html">UMB-DB: A database of partially occluded 3D faces</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>29%</td><td>45</td><td>13</td><td>32</td><td>2</td><td>20</td><td>3</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>31%</td><td>39</td><td>12</td><td>27</td><td>2</td><td>27</td><td>7</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td></td><td></td><td></td><td></td><td>16%</td><td>75</td><td>12</td><td>63</td><td>6</td><td>25</td><td>8</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html">Fine-grained evaluation on face detection in the wild</a></td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Chinese Academy of Sciences</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>13</td><td>4</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="https://doi.org/10.1007/s11263-010-0347-5">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>16%</td><td>77</td><td>12</td><td>39</td><td>3</td><td>32</td><td>0</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html">Consistent Re-identification in a Camera Network</a></td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>45</td><td>12</td><td>33</td><td>7</td><td>34</td><td>1</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td>21%</td><td>58</td><td>12</td><td>46</td><td>7</td><td>40</td><td>1</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>67%</td><td>18</td><td>12</td><td>6</td><td>0</td><td>16</td><td>0</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html">Level Playing Field for Million Scale Face Recognition</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1705.00393.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>41%</td><td>27</td><td>11</td><td>16</td><td>2</td><td>22</td><td>4</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>0</td><td>18</td><td>0</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>25%</td><td>40</td><td>10</td><td>30</td><td>0</td><td>20</td><td>6</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>15</td><td>3</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>50_people_one_question</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>11</td><td>2</td></tr><tr><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td><td>agedb</td><td>AgeDB</td><td><a href="papers/6dcf418c778f528b5792104760f1fbfe90c6dd6a.html">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>82%</td><td>11</td><td>9</td><td>2</td><td>0</td><td>10</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html">Fashion Landmark Detection in the Wild</a></td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>39%</td><td>23</td><td>9</td><td>8</td><td>0</td><td>15</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html">Automated Human Identification Using Ear Imaging</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>13%</td><td>70</td><td>9</td><td>61</td><td>6</td><td>28</td><td>1</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>10</td><td>4</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html">Kinship Verification through Transfer Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>14%</td><td>66</td><td>9</td><td>39</td><td>2</td><td>18</td><td>5</td></tr><tr><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td><td>nudedetection</td><td>Nude Detection</td><td><a href="papers/7ace44190729927e5cb0dd5d363fcae966fe13f7.html">A bag-of-features approach based on Hue-SIFT descriptor for nude detection</a></td><td><a href="http://ieeexplore.ieee.org/document/7077625/">[pdf]</a></td><td>2009 17th European Signal Processing Conference</td><td></td><td></td><td></td><td></td><td>18%</td><td>51</td><td>9</td><td>42</td><td>1</td><td>18</td><td>0</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>20</td><td>8</td><td>12</td><td>2</td><td>16</td><td>0</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928', 'linkType': 'ieee'}">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td></td><td></td><td></td><td></td><td>25%</td><td>32</td><td>8</td><td>24</td><td>3</td><td>16</td><td>1</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>47%</td><td>17</td><td>8</td><td>9</td><td>1</td><td>12</td><td>1</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html">Ear recognition: More than a survey</a></td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td>29%</td><td>24</td><td>7</td><td>17</td><td>0</td><td>11</td><td>0</td></tr><tr><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td><td>camel</td><td>CAMEL</td><td><a href="papers/5801690199c1917fa58c35c3dead177c0b8f9f2d.html">Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf">[pdf]</a></td><td>Remote Sensing</td><td></td><td></td><td></td><td></td><td>37%</td><td>19</td><td>7</td><td>12</td><td>1</td><td>16</td><td>0</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td></td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td>22%</td><td>32</td><td>7</td><td>25</td><td>2</td><td>17</td><td>0</td></tr><tr><td>ec792ad2433b6579f2566c932ee414111e194537</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/ec792ad2433b6579f2566c932ee414111e194537.html">Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1711.08565.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>50%</td><td>14</td><td>7</td><td>7</td><td>1</td><td>11</td><td>0</td></tr><tr><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td><td>pornodb</td><td>Pornography DB</td><td><a href="papers/b92a1ed9622b8268ae3ac9090e25789fc41cc9b8.html">Pooling in image representation: The visual codeword point of view</a></td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>9%</td><td>77</td><td>7</td><td>70</td><td>7</td><td>43</td><td>2</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html">Three-dimensional face recognition: an eigensurface approach</a></td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td>19%</td><td>36</td><td>7</td><td>29</td><td>4</td><td>25</td><td>1</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html">Recognizing disguised faces</a></td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>24</td><td>6</td><td>18</td><td>0</td><td>16</td><td>1</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html">IARPA Janus Benchmark-B Face Dataset</a></td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>24%</td><td>25</td><td>6</td><td>19</td><td>6</td><td>21</td><td>3</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.02459.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td>edu</td><td>South China University of Technology</td><td>23.05020420</td><td>113.39880323</td><td>43%</td><td>14</td><td>6</td><td>8</td><td>3</td><td>5</td><td>7</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>24%</td><td>21</td><td>5</td><td>16</td><td>3</td><td>11</td><td>1</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><a href="https://doi.org/10.1109/TIFS.2014.2361479">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>34.22498270</td><td>-77.86907744</td><td>38%</td><td>13</td><td>5</td><td>3</td><td>0</td><td>3</td><td>3</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><a href="https://doi.org/10.1007/s11042-012-1352-1">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>45.50397610</td><td>-73.57496870</td><td>28%</td><td>18</td><td>5</td><td>6</td><td>0</td><td>7</td><td>4</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html">Re-identify people in wide area camera network</a></td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>9%</td><td>55</td><td>5</td><td>50</td><td>2</td><td>35</td><td>0</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1605.09653.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE transactions on pattern analysis and machine intelligence</td><td></td><td></td><td></td><td></td><td>27%</td><td>15</td><td>4</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td><td>buhmap_db</td><td>BUHMAP-DB </td><td><a href="papers/014b8df0180f33b9fea98f34ae611c6447d761d2.html">Facial feature tracking and expression recognition for sign language</a></td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf">[pdf]</a></td><td>2008 23rd International Symposium on Computer and Information Sciences</td><td></td><td></td><td></td><td></td><td>16%</td><td>25</td><td>4</td><td>21</td><td>1</td><td>10</td><td>2</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html">Adaptive Image Sampling and Windows Classification for On–board Pedestrian Detection</a></td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>44</td><td>4</td><td>40</td><td>1</td><td>21</td><td>0</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>14%</td><td>29</td><td>4</td><td>25</td><td>3</td><td>17</td><td>0</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Michigan State University</td><td>42.71856800</td><td>-84.47791571</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>5</td><td>0</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html">Faces in Places: compound query retrieval</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html">ChaLearn looking at people: A review of events and resources</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1701.02664.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td>30%</td><td>10</td><td>3</td><td>7</td><td>1</td><td>6</td><td>0</td></tr><tr><td>a5acda0e8c0937bfed013e6382da127103e41395</td><td>disfa</td><td>DISFA</td><td><a href="papers/a5acda0e8c0937bfed013e6382da127103e41395.html">Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>38%</td><td>8</td><td>3</td><td>5</td><td>1</td><td>5</td><td>0</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217', 'linkType': 'ieee'}">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td>33%</td><td>9</td><td>3</td><td>6</td><td>2</td><td>9</td><td>0</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html">Large scale unconstrained open set face database</a></td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of Colorado at Colorado Springs</td><td>38.89646790</td><td>-104.80505940</td><td>60%</td><td>5</td><td>3</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html">USED: a large-scale social event detection dataset</a></td><td><a href="http://doi.acm.org/10.1145/2910017.2910624">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html">VADANA: A dense dataset for facial image analysis</a></td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>19%</td><td>16</td><td>3</td><td>13</td><td>0</td><td>6</td><td>6</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf">[pdf]</a></td><td></td><td>edu</td><td>California Institute of Technology</td><td>34.13710185</td><td>-118.12527487</td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>6</td><td>1</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html">Face swapping: automatically replacing faces in photographs</a></td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf">[pdf]</a></td><td>ACM Trans. Graph.</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html">Visual Kinship Recognition of Families in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>67%</td><td>3</td><td>2</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/6dbe8e5121c534339d6e41f8683e85f87e6abf81.html">Clothing Cosegmentation for Shopping Images With Cluttered Background</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>33%</td><td>6</td><td>2</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td><td>ilids_vid_reid</td><td>iLIDS-VID</td><td><a href="papers/99eb4cea0d9bc9fe777a5c5172f8638a37a7f262.html">Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</a></td><td><a href="https://doi.org/10.1109/LSP.2016.2574323">[pdf]</a></td><td>IEEE Signal Processing Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>4</td><td>0</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html">Large Age-Gap face verification by feature injection in deep networks</a></td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>2</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market1203</td><td>Market 1203</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html">Investigating Open-World Person Re-identi cation Using a Drone</a></td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>5</td><td>2</td><td>3</td><td>0</td><td>3</td><td>0</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html">How to Take a Good Selfie?</a></td><td><a href="http://doi.acm.org/10.1145/2733373.2806365">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>czech_news_agency</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>10%</td><td>10</td><td>1</td><td>9</td><td>0</td><td>4</td><td>2</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html">A Multi-modal Graphical Model for Scene Analysis</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td>12%</td><td>8</td><td>1</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td><td>face_research_lab</td><td>Face Research Lab London</td><td><a href="papers/c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8.html">Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</a></td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf">[pdf]</a></td><td>Scientific reports</td><td>edu</td><td>University College London</td><td>51.52316070</td><td>-0.12820370</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><a href="http://doi.acm.org/10.1145/2676440.2676443">[pdf]</a></td><td></td><td>edu</td><td>University of Kentucky</td><td>38.03337420</td><td>-84.50177580</td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html">Indian Face Age Database : A Database for Face Recognition with Age Variation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td><td>megaage</td><td>MegaAge</td><td><a href="papers/d80a3d1f3a438e02a6685e66ee908446766fefa9.html">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1708.09687.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>1</td><td>4</td><td>0</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html">Spoofing faces using makeup: An investigative study</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>43.61581310</td><td>7.06838000</td><td>20%</td><td>5</td><td>1</td><td>4</td><td>0</td><td>1</td><td>2</td></tr><tr><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/fb82681ac5d3487bd8e52dbb3d1fa220eeac855e.html">1 Network Notebook</a></td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>11</td><td>1</td><td>10</td><td>1</td><td>10</td><td>0</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1703.06283.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>8%</td><td>12</td><td>1</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html">ReSEED: social event dEtection dataset</a></td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>17%</td><td>6</td><td>1</td><td>5</td><td>1</td><td>1</td><td>1</td></tr><tr><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709.html">Learning to predict human behaviour in crowded scenes</a></td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>20%</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>0</td></tr><tr><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/9696ad8b164f5e10fcfe23aacf74bd6168aebb15.html">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1712.01443.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>4</td><td>0</td><td>4</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/f152b6ee251cca940dd853c54e6a7b78fbc6b235.html">Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</a></td><td><a href="{'url': 'http://dl.acm.org/citation.cfm?id=3232665', 'linkType': 'acm'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td><td>bjut_3d</td><td>BJUT-3D</td><td><a href="papers/1ed1a49534ad8dd00f81939449f6389cfbc25321.html">A novel face recognition method based on 3D face model</a></td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202">[pdf]</a></td><td>2007 IEEE International Conference on Robotics and Biomimetics (ROBIO)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>1</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4.html">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1805.07193.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html">FDDB: A Benchmark for Face Detection in Unconstrained Settings</a></td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td><td>fei</td><td>FEI</td><td><a href="papers/b6b1b0632eb9d4ab1427278f5e5c46f97753c73d.html">Generalização cartográfica automatizada para um banco de dados cadastral</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html">A Multi - camera video data set for research on High - Definition surveillance</a></td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd.html">High-resolution comprehensive 3-D dynamic database for facial articulation analysis</a></td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>5</td><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/ad62c6e17bc39b4dec20d32f6ac667ae42d2c118.html">A Synchronization Ground Truth for the Jiku Mobile Video Dataset</a></td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html">Component-Based Face Recognition with 3D Morphable Models</a></td><td><a href="http://www.bheisele.com/avbpa2003.pdf">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td>0%</td><td>12</td><td>0</td><td>12</td><td>0</td><td>8</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html">Competitive affective gaming: winning with a smile</a></td><td><a href="http://doi.acm.org/10.1145/2502081.2502115">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html">PETS 2017: Dataset and Challenge</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>8</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku</td><td>PKU</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>1</td><td>0</td></tr><tr><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/c866a2afc871910e3282fd9498dce4ab20f6a332.html">Surveillance Face Recognition Challenge</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.09691.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td><td>scface</td><td>SCface</td><td><a href="papers/f3b84a03985de3890b400b68e2a92c0a00afd9d0.html">Large Variability Surveillance Camera Face Database</a></td><td><span class="gray">[pdf]</a></td><td>2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim)</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.04326.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>377f2b65e6a9300448bdccf678cde59449ecd337</td><td>ufdd</td><td>UFDD</td><td><a href="papers/377f2b65e6a9300448bdccf678cde59449ecd337.html">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.10275.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Johns Hopkins University</td><td>39.32905300</td><td>-76.61942500</td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>447d8893a4bdc29fa1214e53499ffe67b28a6db5</td><td>umd_faces</td><td>UMD</td><td><a href="papers/447d8893a4bdc29fa1214e53499ffe67b28a6db5.html">Electronic Transport in Quantum Confined Systems</a></td><td><a href="http://pdfs.semanticscholar.org/447d/8893a4bdc29fa1214e53499ffe67b28a6db5.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html">Re-identification of pedestrians with variable occlusion and scale</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>10</td><td>0</td><td>10</td><td>2</td><td>6</td><td>0</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><a href="http://doi.acm.org/10.1145/2996913.2996997">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>77c81c13a110a341c140995bedb98101b9e84f7f</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/77c81c13a110a341c140995bedb98101b9e84f7f.html">WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td></td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html">WLFDB: Weakly Labeled Face Databases</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html">YawDD: a yawning detection dataset</a></td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>14</td><td>0</td><td>14</td><td>1</td><td>2</td><td>1</td></tr></table></body></html>
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-<!doctype html><html><head><meta charset='utf-8'><title>All Papers</title><link rel='stylesheet' href='reports.css'></head><body><h2>All Papers</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/fb82681ac5d3487bd8e52dbb3d1fa220eeac855e.html">1 Network Notebook</a></td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>11</td><td>1</td><td>10</td><td>1</td><td>10</td><td>0</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866', 'linkType': 'ieee'}">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>50%</td><td>356</td><td>179</td><td>177</td><td>21</td><td>299</td><td>3</td></tr><tr><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2f5d44dc3e1b5955942133ff872ebd31716ec604.html">2D and 3D face recognition: A survey</a></td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>15%</td><td>389</td><td>57</td><td>332</td><td>28</td><td>198</td><td>17</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><a href="http://doi.acm.org/10.1145/2072572.2072590">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>122</td><td>22</td><td>100</td><td>11</td><td>71</td><td>1</td></tr><tr><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/9696ad8b164f5e10fcfe23aacf74bd6168aebb15.html">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1712.01443.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>4</td><td>0</td><td>4</td><td>0</td><td>2</td><td>0</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>31%</td><td>39</td><td>12</td><td>27</td><td>2</td><td>27</td><td>7</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td>41%</td><td>323</td><td>131</td><td>192</td><td>29</td><td>221</td><td>25</td></tr><tr><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/c34532fe6bfbd1e6df477c9ffdbb043b77e7804d.html">A 3D Morphable Eye Region Model for Gaze Estimation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>67%</td><td>24</td><td>16</td><td>8</td><td>0</td><td>18</td><td>4</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html">A 3D facial expression database for facial behavior research</a></td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>24%</td><td>555</td><td>131</td><td>424</td><td>47</td><td>283</td><td>48</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td>21%</td><td>58</td><td>12</td><td>46</td><td>7</td><td>40</td><td>1</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td>edu</td><td>Jacobs University</td><td>53.41291480</td><td>-2.96897915</td><td>44%</td><td>52</td><td>23</td><td>29</td><td>5</td><td>37</td><td>4</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html">A Multi-modal Graphical Model for Scene Analysis</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td>12%</td><td>8</td><td>1</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>0</td><td>18</td><td>0</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/ad62c6e17bc39b4dec20d32f6ac667ae42d2c118.html">A Synchronization Ground Truth for the Jiku Mobile Video Dataset</a></td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1605.09653.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE transactions on pattern analysis and machine intelligence</td><td></td><td></td><td></td><td></td><td>27%</td><td>15</td><td>4</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td><td>nudedetection</td><td>Nude Detection</td><td><a href="papers/7ace44190729927e5cb0dd5d363fcae966fe13f7.html">A bag-of-features approach based on Hue-SIFT descriptor for nude detection</a></td><td><a href="http://ieeexplore.ieee.org/document/7077625/">[pdf]</a></td><td>2009 17th European Signal Processing Conference</td><td></td><td></td><td></td><td></td><td>18%</td><td>51</td><td>9</td><td>42</td><td>1</td><td>18</td><td>0</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html">A data-driven approach to cleaning large face datasets</a></td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>46%</td><td>123</td><td>56</td><td>67</td><td>6</td><td>95</td><td>21</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>36%</td><td>151</td><td>54</td><td>97</td><td>7</td><td>85</td><td>26</td></tr><tr><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td><td>bjut_3d</td><td>BJUT-3D</td><td><a href="papers/1ed1a49534ad8dd00f81939449f6389cfbc25321.html">A novel face recognition method based on 3D face model</a></td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202">[pdf]</a></td><td>2007 IEEE International Conference on Robotics and Biomimetics (ROBIO)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>1</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html">Adaptive Image Sampling and Windows Classification for On–board Pedestrian Detection</a></td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>44</td><td>4</td><td>40</td><td>1</td><td>21</td><td>0</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>73</td><td>26</td><td>47</td><td>6</td><td>39</td><td>16</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html">Age and Gender Estimation of Unfiltered Faces</a></td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td></td><td></td><td></td><td></td><td>43%</td><td>168</td><td>72</td><td>96</td><td>7</td><td>89</td><td>53</td></tr><tr><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td><td>agedb</td><td>AgeDB</td><td><a href="papers/6dcf418c778f528b5792104760f1fbfe90c6dd6a.html">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>82%</td><td>11</td><td>9</td><td>2</td><td>0</td><td>10</td><td>0</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>imm_face</td><td>IMM Face Dataset</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>muct</td><td>MUCT</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td><td>face_research_lab</td><td>Face Research Lab London</td><td><a href="papers/c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8.html">Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</a></td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf">[pdf]</a></td><td>Scientific reports</td><td>edu</td><td>University College London</td><td>51.52316070</td><td>-0.12820370</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html">Appearance-based gaze estimation in the wild</a></td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Max Planck Institute for Informatics</td><td>49.25795660</td><td>7.04577417</td><td>38%</td><td>138</td><td>52</td><td>86</td><td>3</td><td>94</td><td>7</td></tr><tr><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td><td>camel</td><td>CAMEL</td><td><a href="papers/5801690199c1917fa58c35c3dead177c0b8f9f2d.html">Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf">[pdf]</a></td><td>Remote Sensing</td><td></td><td></td><td></td><td></td><td>37%</td><td>19</td><td>7</td><td>12</td><td>1</td><td>16</td><td>0</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html">Attribute and simile classifiers for face verification</a></td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>51%</td><td>894</td><td>454</td><td>440</td><td>55</td><td>587</td><td>222</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html">Automated Human Identification Using Ear Imaging</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>13%</td><td>70</td><td>9</td><td>61</td><td>6</td><td>28</td><td>1</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html">Automatic 3D face authentication</a></td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>25%</td><td>95</td><td>24</td><td>71</td><td>8</td><td>60</td><td>2</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="https://doi.org/10.1007/s11263-013-0672-6">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>1</td><td>18</td><td>1</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>60%</td><td>50</td><td>30</td><td>19</td><td>2</td><td>40</td><td>4</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html">Bosphorus Database for 3D Face Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>328</td><td>58</td><td>270</td><td>18</td><td>143</td><td>37</td></tr><tr><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/214c966d1f9c2a4b66f4535d9a0d4078e63a5867.html">Brainwash: A Data System for Feature Engineering</a></td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>57</td><td>34</td><td>23</td><td>2</td><td>50</td><td>0</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html">ChaLearn looking at people: A review of events and resources</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1701.02664.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td>30%</td><td>10</td><td>3</td><td>7</td><td>1</td><td>6</td><td>0</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>47%</td><td>60</td><td>28</td><td>32</td><td>0</td><td>36</td><td>6</td></tr><tr><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/6dbe8e5121c534339d6e41f8683e85f87e6abf81.html">Clothing Cosegmentation for Shopping Images With Cluttered Background</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>33%</td><td>6</td><td>2</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf">[pdf]</a></td><td></td><td>edu</td><td>Kyushu University</td><td>33.59914655</td><td>130.22359848</td><td>36%</td><td>848</td><td>308</td><td>540</td><td>56</td><td>413</td><td>255</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html">Component-Based Face Recognition with 3D Morphable Models</a></td><td><a href="http://www.bheisele.com/avbpa2003.pdf">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td>0%</td><td>12</td><td>0</td><td>12</td><td>0</td><td>8</td><td>0</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf">[pdf]</a></td><td></td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>38%</td><td>999</td><td>380</td><td>619</td><td>75</td><td>555</td><td>252</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html">Consistent Re-identification in a Camera Network</a></td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>45</td><td>12</td><td>33</td><td>7</td><td>34</td><td>1</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html">Deep Face Recognition</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>621</td><td>156</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html">Deep Learning Face Attributes in the Wild</a></td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>42%</td><td>808</td><td>340</td><td>468</td><td>69</td><td>666</td><td>50</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk03</td><td>CUHK03</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>35%</td><td>512</td><td>180</td><td>332</td><td>29</td><td>323</td><td>4</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html">Depth and Appearance for Mobile Scene Analysis</a></td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>25%</td><td>319</td><td>79</td><td>240</td><td>27</td><td>192</td><td>0</td></tr><tr><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td><td>a_pascal_yahoo</td><td>aPascal</td><td><a href="papers/2e384f057211426ac5922f1b33d2aa8df5d51f57.html">Describing objects by their attributes</a></td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>727</td><td>73</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>berkeley_pose</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html">Describing people: A poselet-based approach to attribute classification</a></td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>43%</td><td>221</td><td>96</td><td>125</td><td>14</td><td>160</td><td>23</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf">[pdf]</a></td><td></td><td>edu</td><td>California Institute of Technology</td><td>34.13710185</td><td>-118.12527487</td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>6</td><td>1</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html">Ear recognition: More than a survey</a></td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td>29%</td><td>24</td><td>7</td><td>17</td><td>0</td><td>11</td><td>0</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw_a</td><td>LFW-a</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>42%</td><td>177</td><td>75</td><td>102</td><td>15</td><td>102</td><td>54</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>72</td><td>32</td><td>40</td><td>7</td><td>54</td><td>8</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html">End-to-End Deep Learning for Person Search</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf', 'linkType': 's2'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>34%</td><td>41</td><td>14</td><td>27</td><td>2</td><td>27</td><td>0</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>584</td><td>159</td><td>425</td><td>38</td><td>336</td><td>9</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1703.06283.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>8%</td><td>12</td><td>1</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html">Exploring Models and Data for Image Question Answering</a></td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>191</td><td>83</td><td>108</td><td>12</td><td>163</td><td>1</td></tr><tr><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td><td>gavab_db</td><td>Gavab</td><td><a href="papers/42505464808dfb446f521fc6ff2cfeffd4d68ff1.html">Expression invariant 3D face recognition with a Morphable Model</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376', 'linkType': 'ieee'}">[pdf]</a></td><td>2008 8th IEEE International Conference on Automatic Face & Gesture Recognition</td><td></td><td></td><td></td><td></td><td>29%</td><td>94</td><td>27</td><td>67</td><td>10</td><td>57</td><td>5</td></tr><tr><td>a5acda0e8c0937bfed013e6382da127103e41395</td><td>disfa</td><td>DISFA</td><td><a href="papers/a5acda0e8c0937bfed013e6382da127103e41395.html">Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>38%</td><td>8</td><td>3</td><td>5</td><td>1</td><td>5</td><td>0</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html">FDDB: A Benchmark for Face Detection in Unconstrained Settings</a></td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td><td>sheffield</td><td>Sheffield Face</td><td><a href="papers/3607afdb204de9a5a9300ae98aa4635d9effcda2.html">Face Description with Local Binary Patterns: Application to Face Recognition</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>999</td><td>238</td><td>761</td><td>65</td><td>483</td><td>87</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html">Face detection, pose estimation, and landmark localization in the wild</a></td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of California, Irvine</td><td>33.64319010</td><td>-117.84016494</td><td>52%</td><td>999</td><td>521</td><td>478</td><td>59</td><td>607</td><td>273</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html">Face recognition in unconstrained videos with matched background similarity</a></td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Open University of Israel</td><td>32.77824165</td><td>34.99565673</td><td>50%</td><td>485</td><td>244</td><td>240</td><td>32</td><td>290</td><td>140</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html">Faces in Places: compound query retrieval</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td><td>buhmap_db</td><td>BUHMAP-DB </td><td><a href="papers/014b8df0180f33b9fea98f34ae611c6447d761d2.html">Facial feature tracking and expression recognition for sign language</a></td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf">[pdf]</a></td><td>2008 23rd International Symposium on Computer and Information Sciences</td><td></td><td></td><td></td><td></td><td>16%</td><td>25</td><td>4</td><td>21</td><td>1</td><td>10</td><td>2</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html">Fine-grained evaluation on face detection in the wild</a></td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Chinese Academy of Sciences</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>13</td><td>4</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>51%</td><td>148</td><td>76</td><td>72</td><td>8</td><td>109</td><td>3</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>47%</td><td>17</td><td>8</td><td>9</td><td>1</td><td>12</td><td>1</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td><td>kdef</td><td>KDEF</td><td><a href="papers/93884e46c49f7ae1c7c34046fbc28882f2bd6341.html">Gaze fixation and the neural circuitry of face processing in autism</a></td><td><a href="{'url': 'http://doi.org/10.1038/nn1421', 'linkType': 'nature'}">[pdf]</a></td><td>Nature Neuroscience</td><td></td><td></td><td></td><td></td><td>31%</td><td>608</td><td>190</td><td>418</td><td>92</td><td>463</td><td>0</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td><td>fei</td><td>FEI</td><td><a href="papers/b6b1b0632eb9d4ab1427278f5e5c46f97753c73d.html">Generalização cartográfica automatizada para um banco de dados cadastral</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><a href="http://doi.acm.org/10.1145/2676440.2676443">[pdf]</a></td><td></td><td>edu</td><td>University of Kentucky</td><td>38.03337420</td><td>-84.50177580</td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html">High Five: Recognising human interactions in TV shows</a></td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>91</td><td>31</td><td>60</td><td>11</td><td>64</td><td>1</td></tr><tr><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd.html">High-resolution comprehensive 3-D dynamic database for facial articulation analysis</a></td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>5</td><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf">[pdf]</a></td><td></td><td>edu</td><td>Tohoku University</td><td>38.25309450</td><td>140.87365930</td><td>53%</td><td>91</td><td>48</td><td>43</td><td>5</td><td>60</td><td>15</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html">Histograms of oriented gradients for human detection</a></td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>22%</td><td>999</td><td>217</td><td>782</td><td>67</td><td>520</td><td>22</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html">How to Take a Good Selfie?</a></td><td><a href="http://doi.acm.org/10.1145/2733373.2806365">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>67%</td><td>18</td><td>12</td><td>6</td><td>0</td><td>16</td><td>0</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk01</td><td>CUHK01</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>258</td><td>67</td><td>191</td><td>12</td><td>141</td><td>1</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217', 'linkType': 'ieee'}">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td>33%</td><td>9</td><td>3</td><td>6</td><td>2</td><td>9</td><td>0</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html">IARPA Janus Benchmark-B Face Dataset</a></td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>24%</td><td>25</td><td>6</td><td>19</td><td>6</td><td>21</td><td>3</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td></td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td>22%</td><td>32</td><td>7</td><td>25</td><td>2</td><td>17</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html">Indian Face Age Database : A Database for Face Recognition with Age Variation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>10</td><td>4</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html">Interactive Facial Feature Localization</a></td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>52%</td><td>339</td><td>177</td><td>162</td><td>27</td><td>208</td><td>100</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html">International Conference on Automatic Face and Gesture Recognition The CMU Pose , Illumination , and Expression ( PIE ) Database</a></td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>742</td><td>330</td><td>412</td><td>61</td><td>410</td><td>232</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html">Investigating Open-World Person Re-identi cation Using a Drone</a></td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>5</td><td>2</td><td>3</td><td>0</td><td>3</td><td>0</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td></td><td></td><td></td><td></td><td>16%</td><td>75</td><td>12</td><td>63</td><td>6</td><td>25</td><td>8</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>put_face</td><td>Put Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html">Large Age-Gap face verification by feature injection in deep networks</a></td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>2</td></tr><tr><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td><td>scface</td><td>SCface</td><td><a href="papers/f3b84a03985de3890b400b68e2a92c0a00afd9d0.html">Large Variability Surveillance Camera Face Database</a></td><td><span class="gray">[pdf]</a></td><td>2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim)</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html">Large scale unconstrained open set face database</a></td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of Colorado at Colorado Springs</td><td>38.89646790</td><td>-104.80505940</td><td>60%</td><td>5</td><td>3</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>69a68f9cf874c69e2232f47808016c2736b90c35</td><td>celeba_plus</td><td>CelebFaces+</td><td><a href="papers/69a68f9cf874c69e2232f47808016c2736b90c35.html">Learning Deep Representation for Imbalanced Classification</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>33%</td><td>51</td><td>17</td><td>34</td><td>1</td><td>39</td><td>2</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html">Learning Face Representation from Scratch</a></td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>53%</td><td>436</td><td>233</td><td>203</td><td>32</td><td>284</td><td>115</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html">Learning effective human pose estimation from inaccurate annotation</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>40%</td><td>173</td><td>70</td><td>103</td><td>9</td><td>116</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709.html">Learning to predict human behaviour in crowded scenes</a></td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>20%</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>0</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfw_p</td><td>LFWP</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>53%</td><td>521</td><td>274</td><td>247</td><td>40</td><td>321</td><td>144</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk02</td><td>CUHK02</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html">Locally Aligned Feature Transforms across Views</a></td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>24%</td><td>242</td><td>57</td><td>185</td><td>17</td><td>139</td><td>1</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>146</td><td>49</td><td>97</td><td>6</td><td>96</td><td>0</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>167</td><td>83</td><td>84</td><td>15</td><td>131</td><td>27</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>50_people_one_question</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>11</td><td>2</td></tr><tr><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td><td>coco</td><td>COCO</td><td><a href="papers/696ca58d93f6404fea0fc75c62d1d7b378f47628.html">Microsoft COCO Captions: Data Collection and Evaluation Server</a></td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>46%</td><td>283</td><td>129</td><td>154</td><td>16</td><td>231</td><td>4</td></tr><tr><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/a5a44a32a91474f00a3cda671a802e87c899fbb4.html">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1801.03150.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>25</td><td>0</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>38%</td><td>125</td><td>48</td><td>77</td><td>6</td><td>72</td><td>1</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>20</td><td>0</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>25%</td><td>40</td><td>10</td><td>30</td><td>0</td><td>20</td><td>6</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces_news</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html">Names and faces in the news</a></td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td>41%</td><td>294</td><td>120</td><td>174</td><td>24</td><td>207</td><td>45</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>24%</td><td>21</td><td>5</td><td>16</td><td>3</td><td>11</td><td>1</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html">Nonparametric Object and Scene Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>999</td><td>305</td><td>694</td><td>93</td><td>670</td><td>9</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html">O M 4 . 1 The Subject Database 4 . 2 Experiment Plan 5 . 1 Varying the Overlap 4 Experimental Setup 5 Parameterisation Results</a></td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>999</td><td>214</td><td>785</td><td>96</td><td>550</td><td>57</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html">Object Detection Combining Recognition and Segmentation</a></td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>23%</td><td>101</td><td>23</td><td>78</td><td>11</td><td>58</td><td>0</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html">On a large sequence-based human gait database</a></td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>148</td><td>54</td><td>94</td><td>16</td><td>98</td><td>0</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>68</td><td>30</td><td>38</td><td>8</td><td>49</td><td>7</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market1203</td><td>Market 1203</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html">Overview of the face recognition grand challenge</a></td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>253</td><td>746</td><td>110</td><td>572</td><td>64</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html">PETS 2017: Dataset and Challenge</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>8</td><td>0</td><td>2</td><td>0</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462', 'linkType': 'ieee'}">[pdf]</a></td><td>Face and Gesture 2011</td><td></td><td></td><td></td><td></td><td>32%</td><td>184</td><td>58</td><td>126</td><td>23</td><td>112</td><td>23</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>University of Queensland</td><td>-27.49741805</td><td>153.01316956</td><td>30%</td><td>128</td><td>39</td><td>89</td><td>6</td><td>68</td><td>14</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html">Pedestrian Attribute Recognition At Far Distance</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>80</td><td>37</td><td>43</td><td>2</td><td>51</td><td>3</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>352</td><td>94</td><td>258</td><td>26</td><td>195</td><td>3</td></tr><tr><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td><td>ilids_vid_reid</td><td>iLIDS-VID</td><td><a href="papers/99eb4cea0d9bc9fe777a5c5172f8638a37a7f262.html">Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</a></td><td><a href="https://doi.org/10.1109/LSP.2016.2574323">[pdf]</a></td><td>IEEE Signal Processing Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>4</td><td>0</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html">Person Re-identification in the Wild</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Technology Sydney</td><td>-33.88096510</td><td>151.20107299</td><td>38%</td><td>65</td><td>25</td><td>40</td><td>1</td><td>46</td><td>0</td></tr><tr><td>ec792ad2433b6579f2566c932ee414111e194537</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/ec792ad2433b6579f2566c932ee414111e194537.html">Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1711.08565.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>50%</td><td>14</td><td>7</td><td>7</td><td>1</td><td>11</td><td>0</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html">Personalizing Human Video Pose Estimation</a></td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>32</td><td>14</td><td>18</td><td>2</td><td>27</td><td>0</td></tr><tr><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td><td>pornodb</td><td>Pornography DB</td><td><a href="papers/b92a1ed9622b8268ae3ac9090e25789fc41cc9b8.html">Pooling in image representation: The visual codeword point of view</a></td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>9%</td><td>77</td><td>7</td><td>70</td><td>7</td><td>43</td><td>2</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>707</td><td>223</td><td>484</td><td>62</td><td>482</td><td>18</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html">Presentation and validation of the Radboud Faces Database</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>28%</td><td>446</td><td>127</td><td>319</td><td>43</td><td>307</td><td>19</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html">Pruning training sets for learning of object categories</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308', 'linkType': 'ieee'}">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>58%</td><td>60</td><td>35</td><td>25</td><td>5</td><td>42</td><td>12</td></tr><tr><td>377f2b65e6a9300448bdccf678cde59449ecd337</td><td>ufdd</td><td>UFDD</td><td><a href="papers/377f2b65e6a9300448bdccf678cde59449ecd337.html">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.10275.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Johns Hopkins University</td><td>39.32905300</td><td>-76.61942500</td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>48%</td><td>222</td><td>107</td><td>115</td><td>21</td><td>158</td><td>48</td></tr><tr><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td><td>megaage</td><td>MegaAge</td><td><a href="papers/d80a3d1f3a438e02a6685e66ee908446766fefa9.html">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1708.09687.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>1</td><td>4</td><td>0</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html">RONCHI AND PERONA: DESCRIBING COMMON HUMAN VISUAL ACTIONS IN IMAGES 1 Describing Common Human Visual Actions in Images</a></td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>26</td><td>14</td><td>12</td><td>0</td><td>25</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html">Re-identification of pedestrians with variable occlusion and scale</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>10</td><td>0</td><td>10</td><td>2</td><td>6</td><td>0</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html">Re-identify people in wide area camera network</a></td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>9%</td><td>55</td><td>5</td><td>50</td><td>2</td><td>35</td><td>0</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html">ReSEED: social event dEtection dataset</a></td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>17%</td><td>6</td><td>1</td><td>5</td><td>1</td><td>1</td><td>1</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html">Recognition using visual phrases</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>41%</td><td>233</td><td>95</td><td>138</td><td>18</td><td>174</td><td>5</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html">Recognize complex events from static images by fusing deep channels</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>58%</td><td>45</td><td>26</td><td>19</td><td>1</td><td>30</td><td>12</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html">Recognizing disguised faces</a></td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>24</td><td>6</td><td>18</td><td>0</td><td>16</td><td>1</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>498</td><td>127</td><td>371</td><td>55</td><td>319</td><td>32</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html">Robust Face Landmark Estimation under Occlusion</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>55%</td><td>305</td><td>167</td><td>138</td><td>16</td><td>186</td><td>95</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>14%</td><td>29</td><td>4</td><td>25</td><td>3</td><td>17</td><td>0</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.02459.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td>edu</td><td>South China University of Technology</td><td>23.05020420</td><td>113.39880323</td><td>43%</td><td>14</td><td>6</td><td>8</td><td>3</td><td>5</td><td>7</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.04326.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html">Scalable Person Re-identification: A Benchmark</a></td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>38%</td><td>394</td><td>149</td><td>245</td><td>18</td><td>271</td><td>3</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>109df0e8e5969ddf01e073143e83599228a1163f</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/109df0e8e5969ddf01e073143e83599228a1163f.html">Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</a></td><td><a href="http://dl.acm.org/citation.cfm?id=2337184">[pdf]</a></td><td>2012 39th Annual International Symposium on Computer Architecture (ISCA)</td><td></td><td></td><td></td><td></td><td>25%</td><td>192</td><td>48</td><td>144</td><td>8</td><td>99</td><td>0</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>60%</td><td>48</td><td>29</td><td>19</td><td>2</td><td>45</td><td>2</td></tr><tr><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/f152b6ee251cca940dd853c54e6a7b78fbc6b235.html">Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</a></td><td><a href="{'url': 'http://dl.acm.org/citation.cfm?id=3232665', 'linkType': 'acm'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html">Spoofing faces using makeup: An investigative study</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>43.61581310</td><td>7.06838000</td><td>20%</td><td>5</td><td>1</td><td>4</td><td>0</td><td>1</td><td>2</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Michigan State University</td><td>42.71856800</td><td>-84.47791571</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>5</td><td>0</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>towncenter</td><td>TownCenter</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html">Stable Multi-Target Tracking in Real-Time Surveillance Video (Preprint)</a></td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>310</td><td>64</td><td>246</td><td>24</td><td>177</td><td>4</td></tr><tr><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/c866a2afc871910e3282fd9498dce4ab20f6a332.html">Surveillance Face Recognition Challenge</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.09691.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku</td><td>PKU</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>1</td><td>0</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>m2vts</td><td>m2vts</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html">The CMU Face In Action (FIA) Database</a></td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>55</td><td>16</td><td>39</td><td>5</td><td>38</td><td>7</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td>39%</td><td>83</td><td>32</td><td>51</td><td>2</td><td>62</td><td>3</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>CAFE</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>33</td><td>16</td><td>17</td><td>3</td><td>28</td><td>1</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>20</td><td>8</td><td>12</td><td>2</td><td>16</td><td>0</td></tr><tr><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4.html">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1805.07193.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td></td><td></td><td></td><td></td><td>41%</td><td>975</td><td>403</td><td>572</td><td>65</td><td>460</td><td>345</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html">The MUG facial expression database</a></td><td><a href="http://ieeexplore.ieee.org/document/5617662/">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>40.62984145</td><td>22.95889350</td><td>28%</td><td>68</td><td>19</td><td>49</td><td>5</td><td>28</td><td>19</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>39%</td><td>44</td><td>17</td><td>27</td><td>0</td><td>36</td><td>0</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>a6e695ddd07aad719001c0fc1129328452385949</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/a6e695ddd07aad719001c0fc1129328452385949.html">The New Data and New Challenges in Multimedia Research</a></td><td><span class="gray">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>36%</td><td>160</td><td>57</td><td>103</td><td>11</td><td>105</td><td>4</td></tr><tr><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td><td>voc</td><td>VOC</td><td><a href="papers/abe9f3b91fd26fa1b50cd685c0d20debfb372f73.html">The Pascal Visual Object Classes Challenge: A Retrospective</a></td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>999</td><td>315</td><td>684</td><td>75</td><td>698</td><td>6</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html">The intrinsic memorability of face photographs.</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td>36%</td><td>47</td><td>17</td><td>30</td><td>3</td><td>33</td><td>1</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html">Three-dimensional face recognition: an eigensurface approach</a></td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td>19%</td><td>36</td><td>7</td><td>29</td><td>4</td><td>25</td><td>1</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>15</td><td>3</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1212.0402.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>University of Central Florida</td><td>28.59899755</td><td>-81.19712501</td><td>54%</td><td>999</td><td>535</td><td>464</td><td>73</td><td>708</td><td>212</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html">UMB-DB: A database of partially occluded 3D faces</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>29%</td><td>45</td><td>13</td><td>32</td><td>2</td><td>20</td><td>3</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html">USED: a large-scale social event detection dataset</a></td><td><a href="http://doi.acm.org/10.1145/2910017.2910624">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>czech_news_agency</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>10%</td><td>10</td><td>1</td><td>9</td><td>0</td><td>4</td><td>2</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html">Understanding images of groups of people</a></td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>36%</td><td>202</td><td>72</td><td>130</td><td>12</td><td>126</td><td>24</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928', 'linkType': 'ieee'}">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td></td><td></td><td></td><td></td><td>25%</td><td>32</td><td>8</td><td>24</td><td>3</td><td>16</td><td>1</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html">VADANA: A dense dataset for facial image analysis</a></td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>19%</td><td>16</td><td>3</td><td>13</td><td>0</td><td>6</td><td>6</td></tr><tr><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/eb027969f9310e0ae941e2adee2d42cdf07d938c.html">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1710.08092.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>38%</td><td>56</td><td>21</td><td>35</td><td>6</td><td>50</td><td>3</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html">VQA: Visual Question Answering</a></td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>47%</td><td>731</td><td>344</td><td>387</td><td>47</td><td>628</td><td>4</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html">Violent flows: Real-time detection of violent crowd behavior</a></td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>20%</td><td>83</td><td>17</td><td>66</td><td>6</td><td>42</td><td>2</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>18%</td><td>151</td><td>27</td><td>124</td><td>9</td><td>78</td><td>2</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html">Visual Kinship Recognition of Families in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>67%</td><td>3</td><td>2</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html">WIDER FACE: A Face Detection Benchmark</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.06523.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>53%</td><td>148</td><td>78</td><td>70</td><td>16</td><td>107</td><td>34</td></tr><tr><td>77c81c13a110a341c140995bedb98101b9e84f7f</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/77c81c13a110a341c140995bedb98101b9e84f7f.html">WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td></td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html">WLFDB: Weakly Labeled Face Databases</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>77</td><td>34</td><td>43</td><td>4</td><td>57</td><td>1</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html">Web-based database for facial expression analysis</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td>32%</td><td>440</td><td>142</td><td>298</td><td>44</td><td>258</td><td>82</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><a href="http://doi.acm.org/10.1145/2996913.2996997">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html">YawDD: a yawning detection dataset</a></td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>14</td><td>0</td><td>14</td><td>1</td><td>2</td><td>1</td></tr></table></body></html>
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+<!doctype html><html><head><meta charset='utf-8'><title>All Papers</title><link rel='stylesheet' href='reports.css'></head><body><h2>All Papers</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>fb82681ac5d3487bd8e52dbb3d1fa220eeac855e</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/fb82681ac5d3487bd8e52dbb3d1fa220eeac855e.html">1 Network Notebook</a></td><td><a href="http://pdfs.semanticscholar.org/fb82/681ac5d3487bd8e52dbb3d1fa220eeac855e.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>11</td><td>1</td><td>10</td><td>1</td><td>10</td><td>0</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866', 'linkType': 'ieee'}">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>50%</td><td>356</td><td>179</td><td>177</td><td>21</td><td>299</td><td>3</td></tr><tr><td>2f5d44dc3e1b5955942133ff872ebd31716ec604</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2f5d44dc3e1b5955942133ff872ebd31716ec604.html">2D and 3D face recognition: A survey</a></td><td><a href="http://pdfs.semanticscholar.org/2f5d/44dc3e1b5955942133ff872ebd31716ec604.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>15%</td><td>389</td><td>57</td><td>332</td><td>28</td><td>198</td><td>17</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="{'url': 'http://doi.org/10.1016/j.imavis.2016.01.002', 'linkType': 'doi'}">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>45%</td><td>114</td><td>51</td><td>63</td><td>10</td><td>70</td><td>31</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td>edu</td><td>University of Twente</td><td>52.23801390</td><td>6.85667610</td><td>56%</td><td>285</td><td>159</td><td>125</td><td>28</td><td>188</td><td>82</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><a href="http://doi.acm.org/10.1145/2072572.2072590">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>122</td><td>22</td><td>100</td><td>11</td><td>71</td><td>1</td></tr><tr><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/9696ad8b164f5e10fcfe23aacf74bd6168aebb15.html">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1712.01443.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>4</td><td>0</td><td>4</td><td>0</td><td>2</td><td>0</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>31%</td><td>39</td><td>12</td><td>27</td><td>2</td><td>27</td><td>7</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/6399/37b3a1b8bded3f7e9a40e85bd3770016cf3c.pdf">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td>41%</td><td>323</td><td>131</td><td>192</td><td>29</td><td>221</td><td>25</td></tr><tr><td>c34532fe6bfbd1e6df477c9ffdbb043b77e7804d</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/c34532fe6bfbd1e6df477c9ffdbb043b77e7804d.html">A 3D Morphable Eye Region Model for Gaze Estimation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>67%</td><td>24</td><td>16</td><td>8</td><td>0</td><td>18</td><td>4</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html">A 3D facial expression database for facial behavior research</a></td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>24%</td><td>555</td><td>131</td><td>424</td><td>47</td><td>283</td><td>48</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td>21%</td><td>58</td><td>12</td><td>46</td><td>7</td><td>40</td><td>1</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td>edu</td><td>Jacobs University</td><td>53.41291480</td><td>-2.96897915</td><td>44%</td><td>52</td><td>23</td><td>29</td><td>5</td><td>37</td><td>4</td></tr><tr><td>e3e44385a71a52fd483c58eb3cdf8d03960c0b70</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/e3e44385a71a52fd483c58eb3cdf8d03960c0b70.html">A Hierarchical Graphical Model for Recognizing Human Actions and Interactions in Video</a></td><td><a href="http://pdfs.semanticscholar.org/e3e4/4385a71a52fd483c58eb3cdf8d03960c0b70.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html">A Multi - camera video data set for research on High - Definition surveillance</a></td><td><a href="http://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html">A Multi-modal Graphical Model for Scene Analysis</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2015.139">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td>12%</td><td>8</td><td>1</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/221c/18238b829c12b911706947ab38fd017acef7.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>0</td><td>18</td><td>0</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Twente</td><td>52.23801390</td><td>6.85667610</td><td>59%</td><td>169</td><td>100</td><td>69</td><td>14</td><td>112</td><td>49</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.434">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>32%</td><td>85</td><td>27</td><td>58</td><td>9</td><td>51</td><td>0</td></tr><tr><td>ad62c6e17bc39b4dec20d32f6ac667ae42d2c118</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/ad62c6e17bc39b4dec20d32f6ac667ae42d2c118.html">A Synchronization Ground Truth for the Jiku Mobile Video Dataset</a></td><td><a href="http://pdfs.semanticscholar.org/ad62/c6e17bc39b4dec20d32f6ac667ae42d2c118.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1605.09653.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE transactions on pattern analysis and machine intelligence</td><td></td><td></td><td></td><td></td><td>27%</td><td>15</td><td>4</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td><td>nudedetection</td><td>Nude Detection</td><td><a href="papers/7ace44190729927e5cb0dd5d363fcae966fe13f7.html">A bag-of-features approach based on Hue-SIFT descriptor for nude detection</a></td><td><a href="http://ieeexplore.ieee.org/document/7077625/">[pdf]</a></td><td>2009 17th European Signal Processing Conference</td><td></td><td></td><td></td><td></td><td>18%</td><td>51</td><td>9</td><td>42</td><td>1</td><td>18</td><td>0</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html">A data-driven approach to cleaning large face datasets</a></td><td><a href="https://doi.org/10.1109/ICIP.2014.7025068">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>46%</td><td>123</td><td>56</td><td>67</td><td>6</td><td>95</td><td>21</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>42.08779975</td><td>-75.97066066</td><td>36%</td><td>151</td><td>54</td><td>97</td><td>7</td><td>85</td><td>26</td></tr><tr><td>1ed1a49534ad8dd00f81939449f6389cfbc25321</td><td>bjut_3d</td><td>BJUT-3D</td><td><a href="papers/1ed1a49534ad8dd00f81939449f6389cfbc25321.html">A novel face recognition method based on 3D face model</a></td><td><a href="https://doi.org/10.1109/ROBIO.2007.4522202">[pdf]</a></td><td>2007 IEEE International Conference on Robotics and Biomimetics (ROBIO)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>1</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="http://pdfs.semanticscholar.org/2624/d84503bc2f8e190e061c5480b6aa4d89277a.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>47%</td><td>15</td><td>7</td><td>8</td><td>1</td><td>10</td><td>3</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>251</td><td>748</td><td>110</td><td>509</td><td>113</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html">Adaptive Image Sampling and Windows Classification for On–board Pedestrian Detection</a></td><td><a href="http://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>9%</td><td>44</td><td>4</td><td>40</td><td>1</td><td>21</td><td>0</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><a href="http://pdfs.semanticscholar.org/5d06/437656dd94616d7d87260d5eb77513ded30f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>73</td><td>26</td><td>47</td><td>6</td><td>39</td><td>16</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html">Age and Gender Estimation of Unfiltered Faces</a></td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td></td><td></td><td></td><td></td><td>43%</td><td>168</td><td>72</td><td>96</td><td>7</td><td>89</td><td>53</td></tr><tr><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td><td>agedb</td><td>AgeDB</td><td><a href="papers/6dcf418c778f528b5792104760f1fbfe90c6dd6a.html">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>82%</td><td>11</td><td>9</td><td>2</td><td>0</td><td>10</td><td>0</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>imm_face</td><td>IMM Face Dataset</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>muct</td><td>MUCT</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>53%</td><td>292</td><td>155</td><td>137</td><td>38</td><td>207</td><td>59</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>22%</td><td>88</td><td>19</td><td>43</td><td>4</td><td>42</td><td>2</td></tr><tr><td>c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8</td><td>face_research_lab</td><td>Face Research Lab London</td><td><a href="papers/c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8.html">Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.</a></td><td><a href="http://pdfs.semanticscholar.org/c652/6dd3060d63a6c90e8b7ff340383c4e0e0dd8.pdf">[pdf]</a></td><td>Scientific reports</td><td>edu</td><td>University College London</td><td>51.52316070</td><td>-0.12820370</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html">Appearance-based gaze estimation in the wild</a></td><td><a href="https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Max Planck Institute for Informatics</td><td>49.25795660</td><td>7.04577417</td><td>38%</td><td>138</td><td>52</td><td>86</td><td>3</td><td>94</td><td>7</td></tr><tr><td>5801690199c1917fa58c35c3dead177c0b8f9f2d</td><td>camel</td><td>CAMEL</td><td><a href="papers/5801690199c1917fa58c35c3dead177c0b8f9f2d.html">Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/5801/690199c1917fa58c35c3dead177c0b8f9f2d.pdf">[pdf]</a></td><td>Remote Sensing</td><td></td><td></td><td></td><td></td><td>37%</td><td>19</td><td>7</td><td>12</td><td>1</td><td>16</td><td>0</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html">Attribute and simile classifiers for face verification</a></td><td><a href="http://homes.cs.washington.edu/~neeraj/projects/faceverification/base/papers/nk_iccv2009_attrs.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>51%</td><td>894</td><td>454</td><td>440</td><td>55</td><td>587</td><td>222</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html">Automated Human Identification Using Ear Imaging</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>13%</td><td>70</td><td>9</td><td>61</td><td>6</td><td>28</td><td>1</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html">Automatic 3D face authentication</a></td><td><a href="http://pdfs.semanticscholar.org/2160/788824c4c29ffe213b2cbeb3f52972d73f37.pdf">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td>25%</td><td>95</td><td>24</td><td>71</td><td>8</td><td>60</td><td>2</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="https://doi.org/10.1007/s11263-013-0672-6">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>1</td><td>18</td><td>1</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html">Automatic facial makeup detection with application in face recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>39.65404635</td><td>-79.96475355</td><td>65%</td><td>46</td><td>30</td><td>16</td><td>1</td><td>18</td><td>21</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><a href="https://doi.org/10.1109/CVPR.2015.7299113">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>60%</td><td>50</td><td>30</td><td>19</td><td>2</td><td>40</td><td>4</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html">Bosphorus Database for 3D Face Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>18%</td><td>328</td><td>58</td><td>270</td><td>18</td><td>143</td><td>37</td></tr><tr><td>214c966d1f9c2a4b66f4535d9a0d4078e63a5867</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/214c966d1f9c2a4b66f4535d9a0d4078e63a5867.html">Brainwash: A Data System for Feature Engineering</a></td><td><a href="http://pdfs.semanticscholar.org/ae44/8015b2ff2bd3b8a5c9a3266f954f5af9ffa9.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>57</td><td>34</td><td>23</td><td>2</td><td>50</td><td>0</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td>49%</td><td>49</td><td>24</td><td>25</td><td>0</td><td>18</td><td>22</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html">ChaLearn looking at people: A review of events and resources</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1701.02664.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td>30%</td><td>10</td><td>3</td><td>7</td><td>1</td><td>6</td><td>0</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><a href="https://arxiv.org/pdf/1502.00739v1.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>47%</td><td>60</td><td>28</td><td>32</td><td>0</td><td>36</td><td>6</td></tr><tr><td>6dbe8e5121c534339d6e41f8683e85f87e6abf81</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/6dbe8e5121c534339d6e41f8683e85f87e6abf81.html">Clothing Cosegmentation for Shopping Images With Cluttered Background</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>33%</td><td>6</td><td>2</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://pdfs.semanticscholar.org/4b1d/23d17476fcf78f4cbadf69fb130b1aa627c0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>278</td><td>119</td><td>159</td><td>12</td><td>198</td><td>2</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf">[pdf]</a></td><td></td><td>edu</td><td>Kyushu University</td><td>33.59914655</td><td>130.22359848</td><td>36%</td><td>848</td><td>308</td><td>540</td><td>56</td><td>413</td><td>255</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>-35.27769990</td><td>149.11852700</td><td>33%</td><td>175</td><td>58</td><td>66</td><td>7</td><td>54</td><td>49</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html">Competitive affective gaming: winning with a smile</a></td><td><a href="http://doi.acm.org/10.1145/2502081.2502115">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html">Component-Based Face Recognition with 3D Morphable Models</a></td><td><a href="http://www.bheisele.com/avbpa2003.pdf">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td>0%</td><td>12</td><td>0</td><td>12</td><td>0</td><td>8</td><td>0</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="http://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf">[pdf]</a></td><td></td><td>edu</td><td>Carnegie Mellon University</td><td>37.41021930</td><td>-122.05965487</td><td>38%</td><td>999</td><td>380</td><td>619</td><td>75</td><td>555</td><td>252</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html">Consistent Re-identification in a Camera Network</a></td><td><a href="http://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>45</td><td>12</td><td>33</td><td>7</td><td>34</td><td>1</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html">Context-Aware CNNs for Person Head Detection</a></td><td><a href="http://arxiv.org/pdf/1511.07917v1.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>33%</td><td>27</td><td>9</td><td>18</td><td>1</td><td>22</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html">Crowdsourcing facial expressions for affective-interaction</a></td><td><a href="https://doi.org/10.1016/j.cviu.2016.02.001">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390', 'linkType': 'ieee'}">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td>44%</td><td>102</td><td>45</td><td>54</td><td>6</td><td>62</td><td>28</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="https://doi.org/10.1007/s11263-016-0940-3">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>133</td><td>59</td><td>74</td><td>14</td><td>90</td><td>28</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html">Deep Face Recognition</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>621</td><td>156</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html">Deep Learning Face Attributes in the Wild</a></td><td><a href="http://arxiv.org/pdf/1411.7766v2.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>42%</td><td>808</td><td>340</td><td>468</td><td>69</td><td>666</td><td>50</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780493', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>47%</td><td>150</td><td>71</td><td>79</td><td>4</td><td>111</td><td>8</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk03</td><td>CUHK03</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>35%</td><td>512</td><td>180</td><td>332</td><td>29</td><td>323</td><td>4</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html">Depth and Appearance for Mobile Scene Analysis</a></td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07.pdf/at_download/file">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>25%</td><td>319</td><td>79</td><td>240</td><td>27</td><td>192</td><td>0</td></tr><tr><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td><td>a_pascal_yahoo</td><td>aPascal</td><td><a href="papers/2e384f057211426ac5922f1b33d2aa8df5d51f57.html">Describing objects by their attributes</a></td><td><a href="http://www-2.cs.cmu.edu/~dhoiem/publications/cvpr2009_attributes.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>393</td><td>606</td><td>71</td><td>727</td><td>73</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>berkeley_pose</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html">Describing people: A poselet-based approach to attribute classification</a></td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>43%</td><td>221</td><td>96</td><td>125</td><td>14</td><td>160</td><td>23</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf">[pdf]</a></td><td></td><td>edu</td><td>California Institute of Technology</td><td>34.13710185</td><td>-118.12527487</td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>6</td><td>1</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html">Ear recognition: More than a survey</a></td><td><a href="http://pdfs.semanticscholar.org/84fe/5b4ac805af63206012d29523a1e033bc827e.pdf">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td>29%</td><td>24</td><td>7</td><td>17</td><td>0</td><td>11</td><td>0</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw_a</td><td>LFW-a</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.230">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>42%</td><td>177</td><td>75</td><td>102</td><td>15</td><td>102</td><td>54</td></tr><tr><td>447d8893a4bdc29fa1214e53499ffe67b28a6db5</td><td>umd_faces</td><td>UMD</td><td><a href="papers/447d8893a4bdc29fa1214e53499ffe67b28a6db5.html">Electronic Transport in Quantum Confined Systems</a></td><td><a href="http://pdfs.semanticscholar.org/447d/8893a4bdc29fa1214e53499ffe67b28a6db5.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>72</td><td>32</td><td>40</td><td>7</td><td>54</td><td>8</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html">End-to-End Deep Learning for Person Search</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf', 'linkType': 's2'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>34%</td><td>41</td><td>14</td><td>27</td><td>2</td><td>27</td><td>0</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/6273/b3491e94ea4dd1ce42b791d77bdc96ee73a8.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>584</td><td>159</td><td>425</td><td>38</td><td>336</td><td>9</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="http://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td>20%</td><td>586</td><td>119</td><td>467</td><td>48</td><td>336</td><td>3</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1703.06283.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>8%</td><td>12</td><td>1</td><td>11</td><td>1</td><td>10</td><td>0</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html">Exploring Models and Data for Image Question Answering</a></td><td><a href="http://pdfs.semanticscholar.org/aa79/9c29c0d44ece1864467af520fe70540c069b.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>191</td><td>83</td><td>108</td><td>12</td><td>163</td><td>1</td></tr><tr><td>42505464808dfb446f521fc6ff2cfeffd4d68ff1</td><td>gavab_db</td><td>Gavab</td><td><a href="papers/42505464808dfb446f521fc6ff2cfeffd4d68ff1.html">Expression invariant 3D face recognition with a Morphable Model</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376', 'linkType': 'ieee'}">[pdf]</a></td><td>2008 8th IEEE International Conference on Automatic Face & Gesture Recognition</td><td></td><td></td><td></td><td></td><td>29%</td><td>94</td><td>27</td><td>67</td><td>10</td><td>57</td><td>5</td></tr><tr><td>a5acda0e8c0937bfed013e6382da127103e41395</td><td>disfa</td><td>DISFA</td><td><a href="papers/a5acda0e8c0937bfed013e6382da127103e41395.html">Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672', 'linkType': 'ieee'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>38%</td><td>8</td><td>3</td><td>5</td><td>1</td><td>5</td><td>0</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html">FDDB: A Benchmark for Face Detection in Unconstrained Settings</a></td><td><a href="http://pdfs.semanticscholar.org/75da/1df4ed319926c544eefe17ec8d720feef8c0.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3607afdb204de9a5a9300ae98aa4635d9effcda2</td><td>sheffield</td><td>Sheffield Face</td><td><a href="papers/3607afdb204de9a5a9300ae98aa4635d9effcda2.html">Face Description with Local Binary Patterns: Application to Face Recognition</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.244">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>999</td><td>238</td><td>761</td><td>65</td><td>483</td><td>87</td></tr><tr><td>28312c3a47c1be3a67365700744d3d6665b86f22</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/28312c3a47c1be3a67365700744d3d6665b86f22.html">Face Recognition: A Literature Survey1</a></td><td><a href="http://pdfs.semanticscholar.org/2831/2c3a47c1be3a67365700744d3d6665b86f22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>11%</td><td>999</td><td>105</td><td>265</td><td>32</td><td>217</td><td>39</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html">Face detection, pose estimation, and landmark localization in the wild</a></td><td><a href="http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of California, Irvine</td><td>33.64319010</td><td>-117.84016494</td><td>52%</td><td>999</td><td>521</td><td>478</td><td>59</td><td>607</td><td>273</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html">Face recognition in unconstrained videos with matched background similarity</a></td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/lvfw.pdf">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Open University of Israel</td><td>32.77824165</td><td>34.99565673</td><td>50%</td><td>485</td><td>244</td><td>240</td><td>32</td><td>290</td><td>140</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html">Face swapping: automatically replacing faces in photographs</a></td><td><a href="https://classes.cs.uoregon.edu/16F/cis607photo/faces.pdf">[pdf]</a></td><td>ACM Trans. Graph.</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>48%</td><td>218</td><td>105</td><td>113</td><td>17</td><td>146</td><td>52</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html">Faces in Places: compound query retrieval</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>48%</td><td>383</td><td>182</td><td>201</td><td>25</td><td>259</td><td>60</td></tr><tr><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td><td>buhmap_db</td><td>BUHMAP-DB </td><td><a href="papers/014b8df0180f33b9fea98f34ae611c6447d761d2.html">Facial feature tracking and expression recognition for sign language</a></td><td><a href="http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/buhmap/files/ari2008facialfeaturetracking.pdf">[pdf]</a></td><td>2008 23rd International Symposium on Computer and Information Sciences</td><td></td><td></td><td></td><td></td><td>16%</td><td>25</td><td>4</td><td>21</td><td>1</td><td>10</td><td>2</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html">Fashion Landmark Detection in the Wild</a></td><td><a href="http://pdfs.semanticscholar.org/d8ca/e259c1c5bba0c096f480dc7322bbaebfac1a.pdf">[pdf]</a></td><td></td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>39%</td><td>23</td><td>9</td><td>8</td><td>0</td><td>15</td><td>0</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html">Fine-grained evaluation on face detection in the wild</a></td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Chinese Academy of Sciences</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>13</td><td>4</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>51%</td><td>148</td><td>76</td><td>72</td><td>8</td><td>109</td><td>3</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf">[pdf]</a></td><td></td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>47%</td><td>17</td><td>8</td><td>9</td><td>1</td><td>12</td><td>1</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="http://arxiv.org/abs/1609.06426">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>5</td><td>1</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td>42%</td><td>999</td><td>422</td><td>577</td><td>80</td><td>538</td><td>295</td></tr><tr><td>93884e46c49f7ae1c7c34046fbc28882f2bd6341</td><td>kdef</td><td>KDEF</td><td><a href="papers/93884e46c49f7ae1c7c34046fbc28882f2bd6341.html">Gaze fixation and the neural circuitry of face processing in autism</a></td><td><a href="{'url': 'http://doi.org/10.1038/nn1421', 'linkType': 'nature'}">[pdf]</a></td><td>Nature Neuroscience</td><td></td><td></td><td></td><td></td><td>31%</td><td>608</td><td>190</td><td>418</td><td>92</td><td>463</td><td>0</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html">Genealogical face recognition based on UB KinFace database</a></td><td><a href="https://doi.org/10.1109/CVPRW.2011.5981801">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>42.93362780</td><td>-78.88394479</td><td>13%</td><td>30</td><td>4</td><td>26</td><td>1</td><td>9</td><td>5</td></tr><tr><td>b6b1b0632eb9d4ab1427278f5e5c46f97753c73d</td><td>fei</td><td>FEI</td><td><a href="papers/b6b1b0632eb9d4ab1427278f5e5c46f97753c73d.html">Generalização cartográfica automatizada para um banco de dados cadastral</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html">Generic object recognition with boosting</a></td><td><a href="http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/Reprint_Vol_28_No_3_2006.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>24%</td><td>286</td><td>69</td><td>217</td><td>16</td><td>189</td><td>0</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><a href="http://doi.acm.org/10.1145/2676440.2676443">[pdf]</a></td><td></td><td>edu</td><td>University of Kentucky</td><td>38.03337420</td><td>-84.50177580</td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html">High Five: Recognising human interactions in TV shows</a></td><td><a href="http://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>91</td><td>31</td><td>60</td><td>11</td><td>64</td><td>1</td></tr><tr><td>24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd.html">High-resolution comprehensive 3-D dynamic database for facial articulation analysis</a></td><td><a href="http://www.researchgate.net/profile/Wei_Quan3/publication/221430048_High-resolution_comprehensive_3-D_dynamic_database_for_facial_articulation_analysis/links/0deec534309495805d000000.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>5</td><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="http://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf">[pdf]</a></td><td></td><td>edu</td><td>Tohoku University</td><td>38.25309450</td><td>140.87365930</td><td>53%</td><td>91</td><td>48</td><td>43</td><td>5</td><td>60</td><td>15</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html">Histograms of oriented gradients for human detection</a></td><td><a href="http://nichol.as/papers/Dalai/Histograms%20of%20oriented%20gradients%20for%20human%20detection.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>22%</td><td>999</td><td>217</td><td>782</td><td>67</td><td>520</td><td>22</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html">How to Take a Good Selfie?</a></td><td><a href="http://doi.acm.org/10.1145/2733373.2806365">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>9</td><td>2</td><td>7</td><td>0</td><td>5</td><td>0</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>67%</td><td>18</td><td>12</td><td>6</td><td>0</td><td>16</td><td>0</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk01</td><td>CUHK01</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>258</td><td>67</td><td>191</td><td>12</td><td>141</td><td>1</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217', 'linkType': 'ieee'}">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td>33%</td><td>9</td><td>3</td><td>6</td><td>2</td><td>9</td><td>0</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html">IARPA Janus Benchmark-B Face Dataset</a></td><td><a href="http://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>24%</td><td>25</td><td>6</td><td>19</td><td>6</td><td>21</td><td>3</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td></td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td>22%</td><td>32</td><td>7</td><td>25</td><td>2</td><td>17</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html">Indian Face Age Database : A Database for Face Recognition with Age Variation</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>10</td><td>4</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html">Interactive Facial Feature Localization</a></td><td><a href="http://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>40.11116745</td><td>-88.22587665</td><td>52%</td><td>339</td><td>177</td><td>162</td><td>27</td><td>208</td><td>100</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html">International Conference on Automatic Face and Gesture Recognition The CMU Pose , Illumination , and Expression ( PIE ) Database</a></td><td><a href="http://pdfs.semanticscholar.org/4d42/3acc78273b75134e2afd1777ba6d3a398973.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>742</td><td>330</td><td>412</td><td>61</td><td>410</td><td>232</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html">Investigating Open-World Person Re-identi cation Using a Drone</a></td><td><a href="http://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>5</td><td>2</td><td>3</td><td>0</td><td>3</td><td>0</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><a href="https://doi.org/10.1109/TIFS.2014.2361479">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>34.22498270</td><td>-77.86907744</td><td>38%</td><td>13</td><td>5</td><td>3</td><td>0</td><td>3</td><td>3</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="http://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td></td><td></td><td></td><td></td><td>20%</td><td>20</td><td>4</td><td>16</td><td>2</td><td>11</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><a href="https://doi.org/10.1109/BTAS.2013.6712710">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of North Carolina Wilmington</td><td>34.23755810</td><td>-77.92701290</td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>2</td><td>3</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td></td><td></td><td></td><td></td><td>16%</td><td>75</td><td>12</td><td>63</td><td>6</td><td>25</td><td>8</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html">Kinship Verification through Transfer Learning</a></td><td><a href="http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>14%</td><td>66</td><td>9</td><td>39</td><td>2</td><td>18</td><td>5</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf">[pdf]</a></td><td></td><td>edu</td><td>University of Massachusetts</td><td>42.38897850</td><td>-72.52869870</td><td>41%</td><td>116</td><td>47</td><td>46</td><td>4</td><td>62</td><td>19</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>put_face</td><td>Put Face</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html">Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="http://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>472</td><td>526</td><td>71</td><td>619</td><td>260</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html">Labeled Faces in the Wild: A Survey</a></td><td><a href="http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>99</td><td>29</td><td>70</td><td>9</td><td>63</td><td>12</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html">Large Age-Gap face verification by feature injection in deep networks</a></td><td><a href="http://pdfs.semanticscholar.org/0d2d/d4fc016cb6a517d8fb43a7cc3ff62964832e.pdf">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>2</td></tr><tr><td>f3b84a03985de3890b400b68e2a92c0a00afd9d0</td><td>scface</td><td>SCface</td><td><a href="papers/f3b84a03985de3890b400b68e2a92c0a00afd9d0.html">Large Variability Surveillance Camera Face Database</a></td><td><span class="gray">[pdf]</a></td><td>2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim)</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html">Large scale unconstrained open set face database</a></td><td><a href="http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>University of Colorado at Colorado Springs</td><td>38.89646790</td><td>-104.80505940</td><td>60%</td><td>5</td><td>3</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1408.3967.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>33%</td><td>105</td><td>35</td><td>50</td><td>8</td><td>55</td><td>16</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1408.3967.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>33%</td><td>105</td><td>35</td><td>50</td><td>8</td><td>55</td><td>16</td></tr><tr><td>69a68f9cf874c69e2232f47808016c2736b90c35</td><td>celeba_plus</td><td>CelebFaces+</td><td><a href="papers/69a68f9cf874c69e2232f47808016c2736b90c35.html">Learning Deep Representation for Imbalanced Classification</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>33%</td><td>51</td><td>17</td><td>34</td><td>1</td><td>39</td><td>2</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html">Learning Face Representation from Scratch</a></td><td><a href="http://pdfs.semanticscholar.org/b8a2/0ed7e74325da76d7183d1ab77b082a92b447.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>53%</td><td>436</td><td>233</td><td>203</td><td>32</td><td>284</td><td>115</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>expw</td><td>ExpW</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html">Learning Social Relation Traits from Face Images</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>30%</td><td>20</td><td>6</td><td>14</td><td>5</td><td>15</td><td>0</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html">Learning effective human pose estimation from inaccurate annotation</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>40%</td><td>173</td><td>70</td><td>103</td><td>9</td><td>116</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html">Learning to parse images of articulated bodies</a></td><td><a href="http://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>373</td><td>117</td><td>256</td><td>30</td><td>238</td><td>2</td></tr><tr><td>c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709.html">Learning to predict human behaviour in crowded scenes</a></td><td><a href="http://pdfs.semanticscholar.org/c9bd/a86e23cab9e4f15ea0c4cbb6cc02b9dfb709.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>20%</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>0</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html">Level Playing Field for Million Scale Face Recognition</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1705.00393.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>41%</td><td>27</td><td>11</td><td>16</td><td>2</td><td>22</td><td>4</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/dd1d/51c3a59cb71cbfe1433ebeb4d973f7f9ddc1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>22%</td><td>193</td><td>42</td><td>133</td><td>15</td><td>101</td><td>2</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfw_p</td><td>LFWP</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995602">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>Columbia University</td><td>40.84198360</td><td>-73.94368971</td><td>53%</td><td>521</td><td>274</td><td>247</td><td>40</td><td>321</td><td>144</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk02</td><td>CUHK02</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html">Locally Aligned Feature Transforms across Views</a></td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>24%</td><td>242</td><td>57</td><td>185</td><td>17</td><td>139</td><td>1</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>146</td><td>49</td><td>97</td><td>6</td><td>96</td><td>0</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td>46%</td><td>424</td><td>195</td><td>229</td><td>27</td><td>231</td><td>155</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>167</td><td>83</td><td>84</td><td>15</td><td>131</td><td>27</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>2</td><td>0</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>50_people_one_question</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>0</td><td>11</td><td>2</td></tr><tr><td>696ca58d93f6404fea0fc75c62d1d7b378f47628</td><td>coco</td><td>COCO</td><td><a href="papers/696ca58d93f6404fea0fc75c62d1d7b378f47628.html">Microsoft COCO Captions: Data Collection and Evaluation Server</a></td><td><a href="http://pdfs.semanticscholar.org/ba95/81c33a7eebe87c50e61763e4c8d1723538f2.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>46%</td><td>283</td><td>129</td><td>154</td><td>16</td><td>231</td><td>4</td></tr><tr><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/a5a44a32a91474f00a3cda671a802e87c899fbb4.html">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1801.03150.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>25</td><td>0</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_andriluka.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>302</td><td>76</td><td>226</td><td>32</td><td>199</td><td>1</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>25%</td><td>138</td><td>35</td><td>103</td><td>8</td><td>76</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html">Multi-cue onboard pedestrian detection</a></td><td><a href="https://www.mpi-inf.mpg.de/fileadmin/inf/d2/wojek/poster_cwojek_cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>19%</td><td>217</td><td>41</td><td>176</td><td>14</td><td>131</td><td>1</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><a href="http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_CVPR_2013.pdf">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>38%</td><td>125</td><td>48</td><td>77</td><td>6</td><td>72</td><td>1</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.383">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>60%</td><td>25</td><td>15</td><td>10</td><td>2</td><td>20</td><td>0</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>25%</td><td>40</td><td>10</td><td>30</td><td>0</td><td>20</td><td>6</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces_news</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html">Names and faces in the news</a></td><td><a href="http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/berg_names_and_faces.pdf">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td>41%</td><td>294</td><td>120</td><td>174</td><td>24</td><td>207</td><td>45</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="http://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>24%</td><td>21</td><td>5</td><td>16</td><td>3</td><td>11</td><td>1</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html">Nonparametric Object and Scene Recognition</a></td><td><a href="http://pdfs.semanticscholar.org/31b5/8ced31f22eab10bd3ee2d9174e7c14c27c01.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>31%</td><td>999</td><td>305</td><td>694</td><td>93</td><td>670</td><td>9</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html">O M 4 . 1 The Subject Database 4 . 2 Experiment Plan 5 . 1 Varying the Overlap 4 Experimental Setup 5 Parameterisation Results</a></td><td><a href="http://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>999</td><td>214</td><td>785</td><td>96</td><td>550</td><td>57</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html">Object Detection Combining Recognition and Segmentation</a></td><td><a href="http://pdfs.semanticscholar.org/f531/a554cade14b9b340de6730683a28c292dd74.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>23%</td><td>101</td><td>23</td><td>78</td><td>11</td><td>58</td><td>0</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html">On a large sequence-based human gait database</a></td><td><a href="http://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>148</td><td>54</td><td>94</td><td>16</td><td>98</td><td>0</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.532">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>68</td><td>30</td><td>38</td><td>8</td><td>49</td><td>7</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market1203</td><td>Market 1203</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="http://pdfs.semanticscholar.org/a7fe/834a0af614ce6b50dc093132b031dd9a856b.pdf">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>3</td><td>0</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html">Overview of the face recognition grand challenge</a></td><td><a href="http://www3.nd.edu/~kwb/PhillipsEtAlCVPR_2005.pdf">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>25%</td><td>999</td><td>253</td><td>746</td><td>110</td><td>572</td><td>64</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html">PETS 2017: Dataset and Challenge</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td>0%</td><td>8</td><td>0</td><td>8</td><td>0</td><td>2</td><td>0</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462', 'linkType': 'ieee'}">[pdf]</a></td><td>Face and Gesture 2011</td><td></td><td></td><td></td><td></td><td>32%</td><td>184</td><td>58</td><td>126</td><td>23</td><td>112</td><td>23</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><a href="http://conradsanderson.id.au/pdfs/wong_face_selection_cvpr_biometrics_2011.pdf">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>University of Queensland</td><td>-27.49741805</td><td>153.01316956</td><td>30%</td><td>128</td><td>39</td><td>89</td><td>6</td><td>68</td><td>14</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td>38%</td><td>26</td><td>10</td><td>16</td><td>1</td><td>13</td><td>2</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html">Pedestrian Attribute Recognition At Far Distance</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>80</td><td>37</td><td>43</td><td>2</td><td>51</td><td>3</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>10%</td><td>999</td><td>96</td><td>355</td><td>26</td><td>252</td><td>5</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.ucsd.edu/~pdollar/files/papers/DollarCVPR09peds.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>17%</td><td>519</td><td>89</td><td>430</td><td>27</td><td>286</td><td>2</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html">People-tracking-by-detection and people-detection-by-tracking</a></td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>22%</td><td>529</td><td>116</td><td>413</td><td>41</td><td>316</td><td>1</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html">Performance Measures and a Data Set for Multi-target, Multi-camera Tracking</a></td><td><a href="http://pdfs.semanticscholar.org/b5f2/4f49f9a5e47d6601399dc068158ad88d7651.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>136</td><td>58</td><td>78</td><td>6</td><td>107</td><td>0</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="http://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>27%</td><td>352</td><td>94</td><td>258</td><td>26</td><td>195</td><td>3</td></tr><tr><td>99eb4cea0d9bc9fe777a5c5172f8638a37a7f262</td><td>ilids_vid_reid</td><td>iLIDS-VID</td><td><a href="papers/99eb4cea0d9bc9fe777a5c5172f8638a37a7f262.html">Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning</a></td><td><a href="https://doi.org/10.1109/LSP.2016.2574323">[pdf]</a></td><td>IEEE Signal Processing Letters</td><td></td><td></td><td></td><td></td><td>29%</td><td>7</td><td>2</td><td>5</td><td>0</td><td>4</td><td>0</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html">Person Re-identification by Video Ranking</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>25%</td><td>196</td><td>49</td><td>124</td><td>11</td><td>98</td><td>1</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html">Person Re-identification in the Wild</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.357">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Technology Sydney</td><td>-33.88096510</td><td>151.20107299</td><td>38%</td><td>65</td><td>25</td><td>40</td><td>1</td><td>46</td><td>0</td></tr><tr><td>ec792ad2433b6579f2566c932ee414111e194537</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/ec792ad2433b6579f2566c932ee414111e194537.html">Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1711.08565.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>50%</td><td>14</td><td>7</td><td>7</td><td>1</td><td>11</td><td>0</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html">Personalizing Human Video Pose Estimation</a></td><td><a href="http://arxiv.org/pdf/1511.06676v1.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>44%</td><td>32</td><td>14</td><td>18</td><td>2</td><td>27</td><td>0</td></tr><tr><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td><td>pornodb</td><td>Pornography DB</td><td><a href="papers/b92a1ed9622b8268ae3ac9090e25789fc41cc9b8.html">Pooling in image representation: The visual codeword point of view</a></td><td><a href="http://pdfs.semanticscholar.org/b92a/1ed9622b8268ae3ac9090e25789fc41cc9b8.pdf">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td>9%</td><td>77</td><td>7</td><td>70</td><td>7</td><td>43</td><td>2</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><a href="http://vision.stanford.edu/teaching/cs231b_spring1213/papers/ICCV09_BourdevMalik.pdf">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>707</td><td>223</td><td>484</td><td>62</td><td>482</td><td>18</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html">Presentation and validation of the Radboud Faces Database</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>28%</td><td>446</td><td>127</td><td>319</td><td>43</td><td>307</td><td>19</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html">Pruning training sets for learning of object categories</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308', 'linkType': 'ieee'}">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td>58%</td><td>60</td><td>35</td><td>25</td><td>5</td><td>42</td><td>12</td></tr><tr><td>377f2b65e6a9300448bdccf678cde59449ecd337</td><td>ufdd</td><td>UFDD</td><td><a href="papers/377f2b65e6a9300448bdccf678cde59449ecd337.html">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.10275.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Johns Hopkins University</td><td>39.32905300</td><td>-76.61942500</td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>48%</td><td>222</td><td>107</td><td>115</td><td>21</td><td>158</td><td>48</td></tr><tr><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td><td>megaage</td><td>MegaAge</td><td><a href="papers/d80a3d1f3a438e02a6685e66ee908446766fefa9.html">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1708.09687.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>25%</td><td>4</td><td>1</td><td>3</td><td>1</td><td>4</td><td>0</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html">RONCHI AND PERONA: DESCRIBING COMMON HUMAN VISUAL ACTIONS IN IMAGES 1 Describing Common Human Visual Actions in Images</a></td><td><a href="http://pdfs.semanticscholar.org/b38d/cf5fa5174c0d718d65cc4f3889b03c4a21df.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>26</td><td>14</td><td>12</td><td>0</td><td>25</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html">Re-identification of pedestrians with variable occlusion and scale</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130477">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>0%</td><td>10</td><td>0</td><td>10</td><td>2</td><td>6</td><td>0</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html">Re-identify people in wide area camera network</a></td><td><a href="https://doi.org/10.1109/CVPRW.2012.6239203">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>9%</td><td>55</td><td>5</td><td>50</td><td>2</td><td>35</td><td>0</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html">ReSEED: social event dEtection dataset</a></td><td><a href="https://pub.uni-bielefeld.de/download/2663466/2686734">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>17%</td><td>6</td><td>1</td><td>5</td><td>1</td><td>1</td><td>1</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477586">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html">Recognition using visual phrases</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td>41%</td><td>233</td><td>95</td><td>138</td><td>18</td><td>174</td><td>5</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html">Recognize complex events from static images by fusing deep channels</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiong_Recognize_Complex_Events_2015_CVPR_paper.pdf">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Shenzhen Institutes of Advanced Technology</td><td>22.59805605</td><td>113.98533784</td><td>58%</td><td>45</td><td>26</td><td>19</td><td>1</td><td>30</td><td>12</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html">Recognizing disguised faces</a></td><td><a href="http://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>24</td><td>6</td><td>18</td><td>0</td><td>16</td><td>1</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="http://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>26%</td><td>498</td><td>127</td><td>371</td><td>55</td><td>319</td><td>32</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html">Robust Face Landmark Estimation under Occlusion</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298', 'linkType': 'ieee'}">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td>55%</td><td>305</td><td>167</td><td>138</td><td>16</td><td>186</td><td>95</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><a href="https://doi.org/10.1007/s11042-012-1352-1">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>45.50397610</td><td>-73.57496870</td><td>28%</td><td>18</td><td>5</td><td>6</td><td>0</td><td>7</td><td>4</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>14%</td><td>29</td><td>4</td><td>25</td><td>3</td><td>17</td><td>0</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.02459.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td>edu</td><td>South China University of Technology</td><td>23.05020420</td><td>113.39880323</td><td>43%</td><td>14</td><td>6</td><td>8</td><td>3</td><td>5</td><td>7</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.04326.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247998">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>32%</td><td>260</td><td>84</td><td>85</td><td>18</td><td>143</td><td>10</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html">Scalable Person Re-identification: A Benchmark</a></td><td><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ICCV15-ReIDDataset.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>38%</td><td>394</td><td>149</td><td>245</td><td>18</td><td>271</td><td>3</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788', 'linkType': 'ieee'}">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>42.36782045</td><td>-71.12666653</td><td>50%</td><td>50</td><td>25</td><td>25</td><td>3</td><td>39</td><td>4</td></tr><tr><td>109df0e8e5969ddf01e073143e83599228a1163f</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/109df0e8e5969ddf01e073143e83599228a1163f.html">Scheduling heterogeneous multi-cores through performance impact estimation (PIE)</a></td><td><a href="http://dl.acm.org/citation.cfm?id=2337184">[pdf]</a></td><td>2012 39th Annual International Symposium on Computer Architecture (ISCA)</td><td></td><td></td><td></td><td></td><td>25%</td><td>192</td><td>48</td><td>144</td><td>8</td><td>99</td><td>0</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><a href="http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>60%</td><td>48</td><td>29</td><td>19</td><td>2</td><td>45</td><td>2</td></tr><tr><td>f152b6ee251cca940dd853c54e6a7b78fbc6b235</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/f152b6ee251cca940dd853c54e6a7b78fbc6b235.html">Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms</a></td><td><a href="{'url': 'http://dl.acm.org/citation.cfm?id=3232665', 'linkType': 'acm'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html">Spoofing faces using makeup: An investigative study</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>43.61581310</td><td>7.06838000</td><td>20%</td><td>5</td><td>1</td><td>4</td><td>0</td><td>1</td><td>2</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><a href="http://web.cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>Michigan State University</td><td>42.71856800</td><td>-84.47791571</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>5</td><td>0</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>towncenter</td><td>TownCenter</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html">Stable Multi-Target Tracking in Real-Time Surveillance Video (Preprint)</a></td><td><a href="http://pdfs.semanticscholar.org/9361/b784e73e9238d5cefbea5ac40d35d1e3103f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>21%</td><td>310</td><td>64</td><td>246</td><td>24</td><td>177</td><td>4</td></tr><tr><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/c866a2afc871910e3282fd9498dce4ab20f6a332.html">Surveillance Face Recognition Challenge</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1804.09691.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku</td><td>PKU</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="http://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>3</td><td>0</td><td>3</td><td>0</td><td>1</td><td>0</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td>18%</td><td>61</td><td>11</td><td>50</td><td>3</td><td>36</td><td>2</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>m2vts</td><td>m2vts</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="https://doi.org/10.1109/TSMCA.2007.909557">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td>18%</td><td>415</td><td>76</td><td>339</td><td>39</td><td>182</td><td>35</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html">The CMU Face In Action (FIA) Database</a></td><td><a href="http://pdfs.semanticscholar.org/bb47/a03401811f9d2ca2da12138697acbc7b97a3.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>29%</td><td>55</td><td>16</td><td>39</td><td>5</td><td>38</td><td>7</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="http://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td>39%</td><td>83</td><td>32</td><td>51</td><td>2</td><td>62</td><td>3</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>CAFE</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="http://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>33</td><td>16</td><td>17</td><td>3</td><td>28</td><td>1</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1604.01685.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>0</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="http://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>20</td><td>8</td><td>12</td><td>2</td><td>16</td><td>0</td></tr><tr><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4.html">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1805.07193.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td></td><td></td><td></td><td></td><td>41%</td><td>975</td><td>403</td><td>572</td><td>65</td><td>460</td><td>345</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0f0f/cf041559703998abf310e56f8a2f90ee6f21.pdf">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td>11%</td><td>999</td><td>109</td><td>259</td><td>32</td><td>213</td><td>51</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/0c4a/139bb87c6743c7905b29a3cfec27a5130652.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>28%</td><td>112</td><td>31</td><td>81</td><td>12</td><td>76</td><td>4</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>12%</td><td>17</td><td>2</td><td>15</td><td>2</td><td>11</td><td>0</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="http://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>28</td><td>13</td><td>15</td><td>4</td><td>24</td><td>3</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html">The MUG facial expression database</a></td><td><a href="http://ieeexplore.ieee.org/document/5617662/">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>40.62984145</td><td>22.95889350</td><td>28%</td><td>68</td><td>19</td><td>49</td><td>5</td><td>28</td><td>19</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796', 'linkType': 'ieee'}">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>39%</td><td>44</td><td>17</td><td>27</td><td>0</td><td>36</td><td>0</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>47.65432380</td><td>-122.30800894</td><td>55%</td><td>121</td><td>66</td><td>55</td><td>11</td><td>98</td><td>20</td></tr><tr><td>a6e695ddd07aad719001c0fc1129328452385949</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/a6e695ddd07aad719001c0fc1129328452385949.html">The New Data and New Challenges in Multimedia Research</a></td><td><span class="gray">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td>36%</td><td>160</td><td>57</td><td>103</td><td>11</td><td>105</td><td>4</td></tr><tr><td>abe9f3b91fd26fa1b50cd685c0d20debfb372f73</td><td>voc</td><td>VOC</td><td><a href="papers/abe9f3b91fd26fa1b50cd685c0d20debfb372f73.html">The Pascal Visual Object Classes Challenge: A Retrospective</a></td><td><a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc14.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>32%</td><td>999</td><td>315</td><td>684</td><td>75</td><td>698</td><td>6</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>38%</td><td>112</td><td>43</td><td>69</td><td>14</td><td>83</td><td>2</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html">The intrinsic memorability of face photographs.</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td>36%</td><td>47</td><td>17</td><td>30</td><td>3</td><td>33</td><td>1</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html">Three-dimensional face recognition: an eigensurface approach</a></td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td>19%</td><td>36</td><td>7</td><td>29</td><td>4</td><td>25</td><td>1</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="https://doi.org/10.1007/s11263-010-0347-5">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td>16%</td><td>77</td><td>12</td><td>39</td><td>3</td><td>32</td><td>0</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><a href="http://arxiv.org/pdf/1608.01041v1.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>15</td><td>3</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1212.0402.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>CoRR</td><td>edu</td><td>University of Central Florida</td><td>28.59899755</td><td>-81.19712501</td><td>54%</td><td>999</td><td>535</td><td>464</td><td>73</td><td>708</td><td>212</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html">UMB-DB: A database of partially occluded 3D faces</a></td><td><a href="https://doi.org/10.1109/ICCVW.2011.6130509">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>29%</td><td>45</td><td>13</td><td>32</td><td>2</td><td>20</td><td>3</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="http://arxiv.org/abs/1611.01484">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td>54%</td><td>35</td><td>19</td><td>16</td><td>5</td><td>28</td><td>6</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html">USED: a large-scale social event detection dataset</a></td><td><a href="http://doi.acm.org/10.1145/2910017.2910624">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>3</td><td>2</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>czech_news_agency</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>10%</td><td>10</td><td>1</td><td>9</td><td>0</td><td>4</td><td>2</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html">Understanding Kin Relationships in a Photo</a></td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td>24%</td><td>89</td><td>21</td><td>46</td><td>2</td><td>20</td><td>14</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html">Understanding images of groups of people</a></td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/cvpr09.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>36%</td><td>202</td><td>72</td><td>130</td><td>12</td><td>126</td><td>24</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928', 'linkType': 'ieee'}">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td></td><td></td><td></td><td></td><td>25%</td><td>32</td><td>8</td><td>24</td><td>3</td><td>16</td><td>1</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html">VADANA: A dense dataset for facial image analysis</a></td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td>19%</td><td>16</td><td>3</td><td>13</td><td>0</td><td>6</td><td>6</td></tr><tr><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/eb027969f9310e0ae941e2adee2d42cdf07d938c.html">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1710.08092.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td>edu</td><td>University of Oxford</td><td>51.75345380</td><td>-1.25400997</td><td>38%</td><td>56</td><td>21</td><td>35</td><td>6</td><td>50</td><td>3</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html">VQA: Visual Question Answering</a></td><td><a href="http://arxiv.org/pdf/1505.00468v3.pdf">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td>47%</td><td>731</td><td>344</td><td>387</td><td>47</td><td>628</td><td>4</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html">Violent flows: Real-time detection of violent crowd behavior</a></td><td><a href="http://www.wisdom.weizmann.ac.il/mathusers/kliper/Papers/violent_flows.pdf">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td>20%</td><td>83</td><td>17</td><td>66</td><td>6</td><td>42</td><td>2</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><a href="http://vision.cse.psu.edu/publications/pdfs/GeCollinsRubackPAMI2011.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>18%</td><td>151</td><td>27</td><td>124</td><td>9</td><td>78</td><td>2</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html">Visual Kinship Recognition of Families in the Wild</a></td><td><a href="{'url': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841', 'linkType': 'ieee'}">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>67%</td><td>3</td><td>2</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html">WIDER FACE: A Face Detection Benchmark</a></td><td><a href="{'url': 'https://arxiv.org/pdf/1511.06523.pdf', 'linkType': 'arxiv'}">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>22.42031295</td><td>114.20788644</td><td>53%</td><td>148</td><td>78</td><td>70</td><td>16</td><td>107</td><td>34</td></tr><tr><td>77c81c13a110a341c140995bedb98101b9e84f7f</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/77c81c13a110a341c140995bedb98101b9e84f7f.html">WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td></td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html">WLFDB: Weakly Labeled Face Databases</a></td><td><a href="{'url': 'https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf', 'linkType': 's2'}">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>0%</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://pdfs.semanticscholar.org/0dc1/1a37cadda92886c56a6fb5191ded62099c28.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>77</td><td>34</td><td>43</td><td>4</td><td>57</td><td>1</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html">Web-based database for facial expression analysis</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td>32%</td><td>440</td><td>142</td><td>298</td><td>44</td><td>258</td><td>82</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><a href="http://doi.acm.org/10.1145/2996913.2996997">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html">YawDD: a yawning detection dataset</a></td><td><a href="http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>14</td><td>0</td><td>14</td><td>1</td><td>2</td><td>1</td></tr></table></body></html>
\ No newline at end of file
diff --git a/scraper/s2-citation-report.py b/scraper/s2-citation-report.py
index fc52cc35..e0d812d7 100644
--- a/scraper/s2-citation-report.py
+++ b/scraper/s2-citation-report.py
@@ -6,7 +6,6 @@ import math
 import operator
 import click
 import subprocess
-#import builder
 from util import *
 
 DIR_PUBLIC_CITATIONS = "../site/datasets/citations"
@@ -33,12 +32,17 @@ def s2_citation_report():
   print("citations: {}".format(paper_count))
   print("geocoded: {} ({}%)".format(geocode_count, percent(geocode_count, paper_count)))
 
+  # fetch_google_sheet
+
   sts = subprocess.call([
     "s3cmd", "sync",
     DIR_PUBLIC_CITATIONS + '/',
     "s3://megapixels/v1/citations/",
   ])
 
+def write_master_report(fn, title, papers, key):
+  keys, rows = fetch_google_sheet('statistics')
+
 def write_papers_report(fn, title, papers, key, reverse=False):
   sorted_papers = []
   for paper in sorted(papers, key=lambda x: x[key], reverse=reverse):
@@ -154,7 +158,7 @@ def process_paper(row, addresses, success):
         pdf_count += 1
       if has_doi:
         doi_count += 1
-      if citation.data is None:
+      if citation is None or citation.data is None:
         print("Citation missing! {}".format(cite['paperId']))
         continue
       institutions = load_institutions(citationId)
diff --git a/scraper/s2-geocode-spreadsheet.py b/scraper/s2-geocode-spreadsheet.py
index d0fd2050..66607562 100644
--- a/scraper/s2-geocode-spreadsheet.py
+++ b/scraper/s2-geocode-spreadsheet.py
@@ -12,7 +12,7 @@ def s2_geocode_spreadsheet():
   geolocator = geocoders.GoogleV3(os.getenv('MAPS_API_KEY'))
 
   worksheet = fetch_worksheet()
-  rows = fetch_google_sheet()
+  keys, rows = fetch_google_sheet()
   valid_count = 0
   invalid_count = 0
 
diff --git a/scraper/util.py b/scraper/util.py
index a435f91a..c02f018c 100644
--- a/scraper/util.py
+++ b/scraper/util.py
@@ -259,7 +259,7 @@ class AddressBook (object):
   def __init__(self):
     entities = {}
     lookup = {}
-    data = fetch_google_sheet()
+    keys, data = fetch_google_sheet()
     # keys, data = read_csv('reports/pdf_institutions_deduped.csv', keys=True)
     for index, line in enumerate(data):
       if line[0] == line[1] or line[0] not in entities:
@@ -283,16 +283,20 @@ class AddressBook (object):
         return self.data[index]
     return None
 
-def fetch_worksheet():
+def fetch_spreadsheet():
   scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']
   credentials = ServiceAccountCredentials.from_json_keyfile_name('./.creds/Megapixels-ef28f91112a9.json', scope)
   docid = "1denb7TjYsN9igHyvYah7fQ0daABW32Z30lwV7QrDJQc"
   client = gspread.authorize(credentials)
   spreadsheet = client.open_by_key(docid)
-  return spreadsheet.worksheet("institutions")
+  return spreadsheet
 
-def fetch_google_sheet():
-  rows = fetch_worksheet().get_all_values()
+def fetch_worksheet(name="institutions"):
+  spreadsheet = fetch_spreadsheet()
+  return spreadsheet.worksheet(name)
+
+def fetch_google_sheet(name="institutions"):
+  rows = fetch_worksheet(name).get_all_values()
   keys = rows[0]
   lines = rows[1:]
-  return lines
+  return keys, lines
diff --git a/site/datasets/citations/013909077ad843eb6df7a3e8e290cfd5575999d2.json b/site/datasets/citations/013909077ad843eb6df7a3e8e290cfd5575999d2.json
new file mode 100644
index 00000000..c0b64fbc
--- /dev/null
+++ b/site/datasets/citations/013909077ad843eb6df7a3e8e290cfd5575999d2.json
@@ -0,0 +1 @@
+{"id": "013909077ad843eb6df7a3e8e290cfd5575999d2", "paper": {"paperId": "013909077ad843eb6df7a3e8e290cfd5575999d2", "key": "fiw_300", "title": "A Semi-automatic Methodology for Facial Landmark Annotation", "journal": "2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops", "address": "University of Twente", "address_type": "edu", "lat": "52.23801390", "lng": "6.85667610", "pdf_link": "http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf", "report_link": "papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html", "citation_count": 169, "citations_geocoded": 100, "citations_unknown": 69, "citations_empty": 14, "citations_pdf": 112, "citations_doi": 49, "name": "300-W"}, "address": ["University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], "citations": [["Face Alignment by Local Deep Descriptor Regression", "University of Maryland", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Spatial alignment network for facial landmark localization", "School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China", "Beijing Jiaotong University", "Beijing Jiaotong University", "\u5317\u4eac\u4ea4\u901a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100044, \u4e2d\u56fd", "39.94976005", "116.33629046", "edu", ""], ["Robust Statistical Frontalization of Human and Animal Faces", "Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["GAGAN: Geometry-Aware Generative Adversarial Networks", "Middlesex University London", "Middlesex University", "Middlesex University", "Middlesex University, Greyhound Hill, Hendon, The Hyde, London Borough of Barnet, London, Greater London, England, NW4 4JP, UK", "51.59029705", "-0.22963221", "edu", ""], ["Direct Shape Regression Networks for End-to-End Face Alignment", "Xidian University, Xi an, China, 4 University of Pittsburgh, PA, USA", "Xidian University", "Xidian University", "Xidian University (New Campus), 266\u53f7, \u94f6\u674f\u5927\u9053, \u5357\u96f7\u6751, \u957f\u5b89\u533a (Chang'an), \u897f\u5b89\u5e02, \u9655\u897f\u7701, 710126, \u4e2d\u56fd", "34.12358250", "108.83546000", "edu", ""], ["Face Alignment Across Large Poses: A 3D Solution", "Michigan State University", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Adaptive facial feature extraction", "Applied Informatics, Faculty of Technology, Bielefeld University, Germany", "Bielefeld University", "Bielefeld University", "Fachhochschule Bielefeld FB Gestaltung, 3, Lampingstra\u00dfe, Mitte, Bielefeld, Regierungsbezirk Detmold, Nordrhein-Westfalen, 33615, Deutschland", "52.02804210", "8.51148270", "edu", ""], ["Joint Unsupervised Face Alignment and Behaviour Analysis", "Imperial College London, UK", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression", "Nanjing University of Information Science and Technology", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Global supervised descent method", "Carnegie Mellon University, Pittsburgh PA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Adversarially Optimizing Intersection over Union for Object Localization Tasks", "University of Illinois at Chicago", "University of Illinois at Chicago", "University of Illinois at Chicago", "University of Illinois at Chicago, West Taylor Street, Greektown, Chicago, Cook County, Illinois, 60607, USA", "41.86898915", "-87.64856256", "edu", ""], ["Automatic construction Of robust spherical harmonic subspaces", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Cascade for Landmark Guided Semantic Part Segmentation", "School of Computer Science, The University of Nottingham, Nottingham, UK", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Rule based assessment of hearing-impaired children's facial expressions", "Bilgisayar M\u00fchendisli\u011fi, \u0130stanbul Teknik \u00dcniversitesi, \u0130stanbul, Turkey", "Istanbul Technical University", "Istanbul Technical University", "Istanbul Technical University, walking path from main road to Simit restaurant, \u0130stanbul Teknik \u00dcniversitesi, Maslak, F.S.M Mahallesi, Sar\u0131yer, \u0130stanbul, Marmara B\u00f6lgesi, 34469, T\u00fcrkiye", "41.10427915", "29.02231159", "edu", ""], ["Supervised Descent Method", "The Robotics Institute", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment", "Warsaw University of Technology", "Warsaw University of Technology", "Warsaw University of Technology", "Politechnika Warszawska, 1, Plac Politechniki, VIII, \u015ar\u00f3dmie\u015bcie, Warszawa, mazowieckie, 00-661, RP", "52.22165395", "21.00735776", "edu", ""], ["Active Pictorial Structures", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Extended Supervised Descent Method for Robust Face Alignment", "Beijing University of Posts and Telecommunications, Beijing, China", "Beijing University of Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "\u5317\u4eac\u90ae\u7535\u5927\u5b66, \u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Joint facial landmark detection and action estimation based on deep probabilistic random forest", "Department of Computer Science and Engineering, The State University of New York at Buffalo, New York, USA", "SUNY Buffalo", "SUNY Buffalo", "SUNY College at Buffalo, Academic Drive, Elmwood Village, Buffalo, Erie County, New York, 14222, USA", "42.93362780", "-78.88394479", "edu", ""], ["A joint facial point detection method of deep convolutional network and shape regression", "School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China", "University of Electronic Science and Technology of China", "University of Electronic Science and Technology of China", "Columbus, OH 43210, USA", "40.01419050", "-83.03091430", "edu", ""], ["BULAT, TZIMIROPOULOS: CONVOLUTIONAL AGGREGATION OF LOCAL EVIDENCE 1 Convolutional aggregation of local evidence for large pose face alignment", "University of Nottingham", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Supplementary Material for : Active Pictorial Structures", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Fast, Dense Feature SDM on an iPhone", "Queensland University of Technology, Brisbane, Queensland, Australia", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology, Macgregor Lane, Merthyr, South Brisbane, Brisbane, QLD, 4000, Australia", "-27.47715625", "153.02841004", "edu", ""], ["SVM point-based real-time emotion detection", "Institute of VLSI Design, Zhejiang University", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["IoT and Computer Vision Based Driver Safety Monitoring System with Risk Prediction", "Department of Mechanical Engineering, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh", "Chittagong University of Engineering and Technology", "Chittagong University of Engineering and Technology", "Shaheed Tareq Huda Hall, Goal Chattar, \u099a\u099f\u09cd\u099f\u0997\u09cd\u09b0\u09be\u09ae, \u099a\u099f\u09cd\u099f\u0997\u09cd\u09b0\u09be\u09ae \u099c\u09c7\u09b2\u09be, \u099a\u099f\u09cd\u099f\u0997\u09cd\u09b0\u09be\u09ae \u09ac\u09bf\u09ad\u09be\u0997, 4349, \u09ac\u09be\u0982\u09b2\u09be\u09a6\u09c7\u09b6", "22.46221665", "91.96942263", "edu", ""], ["Recognition of Action Units in the Wild with Deep Nets and a New Global-Local Loss", "The Ohio State University", "Ohio State University", "The Ohio State University", "The Ohio State University, Woody Hayes Drive, Columbus, Franklin County, Ohio, 43210, USA", "40.00471095", "-83.02859368", "edu", ""], ["Evaluation of optimization components of a 3D to 2D landmark fitting algorithm for head pose estimation", "University of Amsterdam", "University of Amsterdam", "University of Amsterdam", "Institute for Logic, Language and Computation (ILLC), 107, Science Park, Oost-Watergraafsmeer, Amsterdam, Oost, Amsterdam, Noord-Holland, Nederland, 1098XG, Nederland", "52.35536550", "4.95016440", "edu", ""], ["An automated method for realistic face simulation and facial landmark annotation and its application to active appearance models", "Institute of Imaging and Computer Vision, RWTH Aachen University, Templergraben 55, 52056, Aachen, Germany", "RWTH Aachen University", "RWTH Aachen University", "RWTH Aachen, Mies-van-der-Rohe-Stra\u00dfe, K\u00f6nigsh\u00fcgel, Aachen-Mitte, Aachen, St\u00e4dteregion Aachen, Regierungsbezirk K\u00f6ln, Nordrhein-Westfalen, 52074, Deutschland", "50.77917030", "6.06728733", "edu", ""], ["Project-Out Cascaded Regression with an application to face alignment", "School of Computer Science, University of Nottingham", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Fast and exact bi-directional fitting of active appearance models", "cid:63) Imperial College London, UK", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Face Alignment With Deep Regression", "School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China", "Huazhong University of Science and Technology", "Huazhong University of Science and Technology", "\u534e\u4e2d\u5927, \u73de\u55bb\u8def, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a, \u5173\u4e1c\u8857\u9053, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a\uff08\u6258\u7ba1\uff09, \u6d2a\u5c71\u533a (Hongshan), \u6b66\u6c49\u5e02, \u6e56\u5317\u7701, 430074, \u4e2d\u56fd", "30.50975370", "114.40628810", "edu", ""], ["An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation", "University of California San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["HOG active appearance models", "cid:2)Imperial College London, U.K", "University of Lincoln", "University of Lincoln", "University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge", "School of Computer Science, University of Lincoln, U.K", "University of Lincoln", "University of Lincoln", "University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["Shape initialization without ground truth for face alignment", "National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization", "State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China", "Huazhong University of Science and Technology", "Huazhong University of Science and Technology", "\u534e\u4e2d\u5927, \u73de\u55bb\u8def, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a, \u5173\u4e1c\u8857\u9053, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a\uff08\u6258\u7ba1\uff09, \u6d2a\u5c71\u533a (Hongshan), \u6b66\u6c49\u5e02, \u6e56\u5317\u7701, 430074, \u4e2d\u56fd", "30.50975370", "114.40628810", "edu", ""], ["Locating Facial Landmarks Using Probabilistic Random Forest", "School of Mathematical Sciences, University of Science and Technology of China, Hefei, China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Face Alignment at 3000 FPS via Regressing Local Binary Features", "University of Science and Technology of China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Face frontalization for Alignment and Recognition", "University of Twente", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Bayesian Active Appearance Models", "Imperial College London, United Kingdom", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Multiview Active Shape Models with SIFT Descriptors for the 300-W Face Landmark Challenge", "University of Cape Town", "University of Cape Town", "University of Cape Town", "University of Cape Town, Engineering Mall, Cape Town Ward 59, Cape Town, City of Cape Town, Western Cape, CAPE TOWN, South Africa", "-33.95828745", "18.45997349", "edu", ""], ["Part-based representation and classification for face recognition", "Institute of Computing, University of Campinas (UNICAMP), SP, 13083-852, Brazil", "University of Campinas (UNICAMP)", "Institute of Computing, University of Campinas (UNICAMP), Campinas, SP, 13083-852, Brazil", "Universidade Estadual de Campinas - Av. Albert Einstein, 1251 - Cidade Universit\u00e1ria, Campinas - SP, 13083-852, Brazil", "-22.81483740", "-47.06477080", "edu", ""], ["Recognizing Minimal Facial Sketch by Generating Photorealistic Faces With the Guidance of Descriptive Attributes", "Tsinghua National Lab for Information Science and Technology, Beijing, China", "Tsinghua National Lab for Information Science and Technology, Beijing", "Tsinghua National Lab for Information Science and Technology, Beijing, China", "Shuangqing Rd, Haidian Qu, Beijing Shi, China, 100083", "39.99674580", "116.33229460", "edu", ""], ["Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild", "University of Lincoln, U.K", "University of Lincoln", "University of Lincoln", "University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["Facial landmark detection via cascade multi-channel convolutional neural network", "Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, Shaanxi, China", "Xi'an Jiaotong University", "Xi'an Jiaotong University", "\u897f\u5b89\u4ea4\u901a\u5927\u5b66\u5174\u5e86\u6821\u533a, \u6587\u6cbb\u8def, \u4e50\u5c45\u573a, \u7891\u6797\u533a (Beilin), \u897f\u5b89\u5e02, \u9655\u897f\u7701, 710048, \u4e2d\u56fd", "34.24749490", "108.97898751", "edu", ""], ["Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment", "Tsinghua University", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Beyond Principal Components: Deep Boltzmann Machines for face modeling", "Concordia University, Computer Science and Software Engineering, Montr eal, Qu ebec, Canada", "Concordia University", "Concordia University", "Concordia University, 2811, Northeast Holman Street, Concordia, Portland, Multnomah County, Oregon, 97211, USA", "45.57022705", "-122.63709346", "edu", ""], ["Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks", "University of Denver, Denver, CO", "University of Denver", "University of Denver", "University of Denver, Driscoll Bridge, Denver, Denver County, Colorado, 80208, USA", "39.67665410", "-104.96220300", "edu", ""], ["Extra Facial Landmark Localization via Global Shape Reconstruction", "School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave", "University of Electronic Science and Technology of China", "University of Electronic Science and Technology of China", "Columbus, OH 43210, USA", "40.01419050", "-83.03091430", "edu", ""], ["Attribute preserved face de-identification", "Department of Computer Science and Engineering, Michigan State University, East Lansing 48824, USA", "Michigan State University", "Michigan State University", 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new file mode 100644
index 00000000..57d1550f
--- /dev/null
+++ b/site/datasets/citations/044d9a8c61383312cdafbcc44b9d00d650b21c70.json
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Houston, TX, USA", "University of Houston", "University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["Track Facial Points in Unconstrained Videos", "The University of Texas at Arlington", "Rutgers University", "Rutgers University", "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA", "40.47913175", "-74.43168868", "edu", ""], ["Face alignment with Cascaded Bidirectional LSTM Neural Networks", "School of Computer Science and Engineering, Nanjing University of Science and Technology, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Learning deep representation from coarse to fine for face alignment", "Shanghai Jiao Tong University, China", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Robust facial landmark detection in the wild", "University of Surrey", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Distance Estimation of an Unknown Person from a Portrait", "California Institute of Technology, Pasadena, CA, USA", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Face Alignment Refinement via Exploiting Low-Rank property and Temporal Stability", "Zhejiang University", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["CSE 255 Winter 2015 Assignment 1: Eye Detection using Histogram of Oriented Gradients and Adaboost Classifier", "University of California, San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["Joint Face Alignment and 3D Face Reconstruction", "Michigan State University, East Lansing, MI, U.S.A", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["300 Faces In-The-Wild Challenge: database and results", "image being generated by the model, include Active Appearance", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Spontaneously Emerging Object Part Segmentation", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition", "University of California, San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["A Survey of Automatic Facial 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"-95.34406271", "edu", ""], ["DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild R\u0131za", "University College London, UK", "University College London", "University College London", "UCL Institute of Education, 20, Bedford Way, Holborn, Bloomsbury, London Borough of Camden, London, Greater London, England, WC1H 0AL, UK", "51.52316070", "-0.12820370", "edu", ""], ["Affine-Transformation Parameters Regression for Face Alignment", "National Laboratory for Parallel and Distributed Processing, School of Computer, College of Computer, National University of Defense Technology, Changsha, China", "National University of Defense Technology, China", "National University of Defence Technology, Changsha 410000, China", "\u56fd\u9632\u79d1\u5b66\u6280\u672f\u5927\u5b66, \u4e09\u4e00\u5927\u9053, \u5f00\u798f\u533a, \u5f00\u798f\u533a (Kaifu), \u957f\u6c99\u5e02 / Changsha, \u6e56\u5357\u7701, 410073, \u4e2d\u56fd", "28.22902090", "112.99483204", "edu", ""], ["Constrained Ensemble Initialization for Facial Landmark Tracking in Video", "Language Technology Institute, Carnegie Mellon University, Pittsburgh, PA, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["A comparison of facial landmark detection methods", "Elektrik-Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Trakya \u00dcniversitesi, Edirne, T\u00fcrkiye", "Trakya University, Edirne, Turkey", "Elektrik-Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Trakya \u00dcniversitesi, Edirne, T\u00fcrkiye", "Edirne, Edirne Merkez/Edirne, Turkey", "41.67712970", "26.55571450", "edu", ""], ["Facial Landmark Detection with Tweaked Convolutional Neural Networks", "USC Information Sciences Institute", "Open University of Israel", "Open University of Israel", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 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Features, Classification Tasks, and Databases", "Pediatrics Department, University of South Florida, Tampa, FL, USA", "University of South Florida", "University of South Florida", "University of South Florida, Leroy Collins Boulevard, Tampa, Hillsborough County, Florida, 33620, USA", "28.05999990", "-82.41383619", "edu", ""], ["Convolutional Experts Constrained Local Model for Facial Landmark Detection", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Extra Facial Landmark Localization via Global Shape Reconstruction", "School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave", "University of Electronic Science and Technology of China", "University of Electronic Science and Technology of China", "Columbus, OH 43210, USA", "40.01419050", "-83.03091430", "edu", ""], ["AgeDB: The First Manually Collected, In-the-Wild Age Database", "Middlesex University London", "Middlesex University", "Middlesex University", "Middlesex University, Greyhound Hill, Hendon, The Hyde, London Borough of Barnet, London, Greater London, England, NW4 4JP, UK", "51.59029705", "-0.22963221", "edu", ""], ["PastVision+: Thermovisual Inference of Recent Medicine Intake by Detecting Heated Objects and Cooled Lips", "University of Sk vde, Sweden", "Halmstad University", "Halmstad University", "H\u00f6gskolan i Halmstad, 3, Kristian IV:s v\u00e4g, Larsfrid, Nyhem, Halmstad, Hallands l\u00e4n, G\u00f6taland, 301 18, Sverige", "56.66340325", "12.87929727", "edu", ""], ["Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment", "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Towards a Unified Framework for Pose, Expression, and Occlusion Tolerant Automatic Facial Alignment", "CyLab Biometrics Center and the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Improved CNN-based facial landmarks tracking via ridge regression at 150 Fps on mobile devices", "Tencent Inc", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, 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Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["A Smartphone-Based Automatic Diagnosis System for Facial Nerve Palsy", "Interdisciplinary Program of Bioengineering, Seoul National University, Seoul 03080, Korea", "Seoul National University", "Seoul National University", "\uc11c\uc6b8\ub300\ud559\uad50, \uc11c\ud638\ub3d9\ub85c, \uc11c\ub454\ub3d9, \uad8c\uc120\uad6c, \uc218\uc6d0\uc2dc, \uacbd\uae30, 16614, \ub300\ud55c\ubbfc\uad6d", "37.26728000", "126.98411510", "edu", ""], ["Two-Stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge", "Computer Vision Laboratory, University of Nottingham, Nottingham, UK", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Evaluation of optimization components of a 3D to 2D landmark fitting algorithm for head pose estimation", "University of Amsterdam", "University of Amsterdam", "University of Amsterdam", "Institute for Logic, Language and Computation (ILLC), 107, Science Park, Oost-Watergraafsmeer, Amsterdam, Oost, Amsterdam, Noord-Holland, Nederland, 1098XG, Nederland", "52.35536550", "4.95016440", "edu", ""], ["Adaptive Cascade Regression Model For Robust Face Alignment", "Department of Information and Control, B-DAT Laboratory, Nanjing University of Information and Technology, Nanjing, China", "University of Technology", "Center for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia", "11 University of Technology Sydney 81, Broadway, Ultimo NSW 2007, Australia", "-33.88405040", "151.19922540", "edu", ""], ["Human and sheep facial landmarks localisation by triplet interpolated features", "University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Viewpoint-Consistent 3D Face Alignment", "Snapchat Research, Venice, CA90291", "Robotics Institute", "Robotics Institute", "Institute for Field Robotics, \u0e1b\u0e23\u0e30\u0e0a\u0e32\u0e2d\u0e38\u0e17\u0e34\u0e28, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, \u0e40\u0e02\u0e15\u0e23\u0e32\u0e29\u0e0e\u0e23\u0e4c\u0e1a\u0e39\u0e23\u0e13\u0e30, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, 10140, \u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e44\u0e17\u0e22", "13.65450525", "100.49423171", "edu", ""], ["Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection", "University of Southern California", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", "edu", ""], ["3 D Face Morphable Models \u201c Inthe-Wild \u201d", "University of Oulu, Finland", "University of Oulu", "University of Oulu", "Oulun yliopisto, Biologintie, Linnanmaa, Oulu, Oulun seutukunta, Pohjois-Pohjanmaa, Pohjois-Suomen aluehallintovirasto, Pohjois-Suomi, Manner-Suomi, 90540, Suomi", "65.05921570", "25.46632601", "edu", ""], ["Adaptive facial feature extraction", "Applied Informatics, Faculty of Technology, Bielefeld University, Germany", "Bielefeld University", "Bielefeld University", "Fachhochschule Bielefeld FB Gestaltung, 3, Lampingstra\u00dfe, Mitte, Bielefeld, Regierungsbezirk Detmold, Nordrhein-Westfalen, 33615, Deutschland", "52.02804210", "8.51148270", "edu", ""], ["Consensus of Regression for Occlusion-Robust Facial Feature Localization", "Rutgers University, Piscataway, NJ 08854, USA", "Rutgers University", "Rutgers University", "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA", "40.47913175", "-74.43168868", "edu", ""], ["Quantized Densely Connected U-Nets for Efficient Landmark Localization", "", "SenseTime", "SenseTime", "China, Beijing Shi, Haidian Qu, WuDaoKou, Zhongguancun E Rd, 1\u53f7-7", "39.99300800", "116.32988200", "company", "1 Zhongguancun E Rd, Haidian Qu, China"], ["A Deep Semi-NMF Model for Learning Hidden Representations", "Imperial College London, United Kingdom", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Robust facial landmark tracking via cascade regression", "Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["The 3D Menpo Facial Landmark Tracking Challenge", "University of Exeter, UK", "University of Exeter", "University of Exeter", "University of Exeter, Stocker Road, Exwick, Exeter, Devon, South West England, England, EX4 4QN, UK", "50.73693020", "-3.53647672", "edu", ""], ["Load Balanced GANs for Multi-view Face Image Synthesis", "University of Chinese Academy of Sciences, Beijing, 100049, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Supplementary Material for : Active Pictorial Structures", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Facial landmark localization: Past, present and future", "Dept. of Computer Sciences, ASIA Team, Moulay Ismail University, Faculty of Science and Techniques, BP 509 Boutalamine 52000 Errachidia, Morocco", "Moulay Ismail University", "Moulay Ismail University", "Marjane 2, BP: 298\u060c Meknes 50050, Morocco", "33.85611100", "-5.57439100", "edu", "Moulay Ismail University"], ["Global supervised descent method", "Carnegie Mellon University, Pittsburgh PA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization", "University of Chinese Academy of Sciences (UCAS", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment", "Warsaw University of Technology", "Warsaw University of Technology", "Warsaw University of Technology", "Politechnika Warszawska, 1, Plac Politechniki, VIII, \u015ar\u00f3dmie\u015bcie, Warszawa, mazowieckie, 00-661, RP", "52.22165395", "21.00735776", "edu", ""], ["Unconstrained Face Alignment via Cascaded Compositional Learning", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["The GIST of aligning faces", "School of Information Technology and Electrical Engineering, The University of Queensland, Australia", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Face alignment by coarse-to-fine shape searching", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Rendering of Eyes for Eye-Shape Registration and Gaze Estimation", "", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, 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University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Face Alignment Assisted by Head Pose Estimation", "University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Face alignment using cascade Gaussian process regression trees", "Korea Advanced institute of Science and Technology", "Korea Advanced Institute of Science and Technology", "Korea Advanced Institute of Science and Technology", "\uce74\uc774\uc2a4\ud2b8, 291, \ub300\ud559\ub85c, \uc628\ucc9c2\ub3d9, \uc628\ucc9c\ub3d9, \uc720\uc131\uad6c, \ub300\uc804, 34141, \ub300\ud55c\ubbfc\uad6d", "36.36971910", "127.36253700", "edu", ""], ["Real-time SVM-based emotion recognition algorithm", "Institute of VLSI Design, Zhejiang 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\u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression", "Nanjing University of Information Science and Technology", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Supplementary Material for: Active Pictorial Structures", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, 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diff --git a/site/datasets/citations/0f0fcf041559703998abf310e56f8a2f90ee6f21.json b/site/datasets/citations/0f0fcf041559703998abf310e56f8a2f90ee6f21.json
new file mode 100644
index 00000000..50f98022
--- /dev/null
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University of Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "\u5317\u4eac\u90ae\u7535\u5927\u5b66, \u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization", "Faculty of Electrical Engineering, University of Ljubljana, Tr za ska cesta", "University of Ljubljana", "University of Ljubljana", "UL Fakulteta za ra\u010dunalni\u0161tvo in informatiko, 113, Ve\u010dna pot, Vrta\u010da, Ro\u017ena dolina, Ljubljana, Upravna Enota Ljubljana, Osrednjeslovenska, 1000, Slovenija", "46.05015580", "14.46907327", "edu", ""], ["Heterogeneous Face Recognition", "Michigan State University", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Repetition Suppression and Memory for Faces is Reduced in Adults with Autism Spectrum Conditions", "", "Cardiff University", "Cardiff University", "Cardiff University, Park Place, Castle, Cardiff, Wales, CF, UK", "51.48799610", "-3.17969747", "edu", ""], ["An Image Preprocessing Algorithm for Illumination Invariant Face Recognition", "The Robotics Institute, Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Performance improvement of face recognition algorithms using occluded-region detection", "Graduate School of Information Sciences, Tohoku University, 6-6-05., Aramaki Aza Aoba., Sendai-shi., 980-8579., Japan", "Tohoku University", "Tohoku University", "Tohoku University, \u4e94\u6a4b\u901a, \u9752\u8449\u533a, \u4ed9\u53f0\u5e02, \u5bae\u57ce\u770c, 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"University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["Face Hallucination via Semi-kernel Partial Least Squares", "", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting", "Michigan State University, East Lansing MI", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Data-specific Adaptive Threshold for Face Recognition and Authentication", "", "Institute of Information Science", "Institute of Information Science", "\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240, \u6578\u7406\u5927\u9053, \u4e2d\u7814\u91cc, \u5357\u6e2f\u5b50, 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\u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Displacement Template with Divide-&-Conquer Algorithm for Significantly Improving Descriptor Based Face Recognition Approaches", "Wenzhou University, China", "University of Northern British Columbia", "University of Northern British Columbia", "UNBC, Campus Ring Road, College Heights, Prince George, Regional District of Fraser-Fort George, British Columbia, V2M 5K7, Canada", "53.89256620", "-122.81471592", "edu", ""], ["3D Face Recognition", "", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Pose-Invariant 2 D Face Recognition by Matching Using Graphical Models", "University of Surrey", "University of Surrey", "University of Surrey", "University of Surrey, Spine 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Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "\u5317\u4eac\u90ae\u7535\u5927\u5b66, \u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Optimized Kernel-based Projection Space of Riemannian Manifolds", "", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Sparsely Encoded Local Descriptor for face recognition", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Chinese Academy of Science", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Beijing, China", "39.90419990", "116.40739630", "edu", ""], ["Identification of Individuals from Ears in Real World 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Fisher Discriminant Analysis in Full Eigenspace", "", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Exploring the geo-dependence of human face appearance", "University of Kentucky", "University of Kentucky", "University of Kentucky", "University of Kentucky, Columbia Avenue, Sorority Circle, Lexington, Fayette County, Kentucky, 40508, USA", "38.03337420", "-84.50177580", "edu", ""], ["Quo vadis Face Recognition?", "University of Pittsburgh", "University of Pittsburgh", "University of Pittsburgh", "University of Pittsburgh, Sutherland Drive, West Oakland, PGH, Allegheny County, Pennsylvania, 15240, USA", "40.44415295", "-79.96243993", "edu", ""], ["Towards resolution invariant face recognition in uncontrolled scenarios", "National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University Chengdu, 610065, 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Toronto, Ontario, M5S 1A5, Canada", "43.66333345", "-79.39769975", "edu", ""], ["The Application of Extended Geodesic Distance in Head Poses Estimation", "Institute of Computing Technology", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Focus on quality, predicting FRVT 2006 performance", "", "Colorado State University", "Colorado State University", "Colorado State University, West Pitkin Street, Woodwest, Fort Collins, Larimer County, Colorado, 80526-2002, USA", "40.57093580", "-105.08655256", "edu", ""], ["Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns", "The Computer Laboratory, University of Cambridge, Cambridge, UK", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson 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Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["Face Detection: a Survey", "School of Computer Science and Technology, Harbin Institute of", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Overview of the Multiple Biometrics Grand Challenge", "", "National Institute of Standards and Technology", "National Institute of Standards and Technology", "National Institute of Standards and Technology, Summer Walk Drive, Diamond Farms, Gaithersburg, Montgomery County, Maryland, 20878, USA", "39.12549380", "-77.22293475", "edu", ""], ["Multiscale binarised statistical image features for symmetric face matching using multiple descriptor fusion based on class-specific LDA", "Department of Electrical Engineering, Faculty of Engineering, 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\ No newline at end of file
diff --git a/site/datasets/citations/18c72175ddbb7d5956d180b65a96005c100f6014.json b/site/datasets/citations/18c72175ddbb7d5956d180b65a96005c100f6014.json
new file mode 100644
index 00000000..6498af76
--- /dev/null
+++ b/site/datasets/citations/18c72175ddbb7d5956d180b65a96005c100f6014.json
@@ -0,0 +1 @@
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Eng, National United University, Miaoli, Taiwan", "National Chiao Tung University", "National Chiao Tung University", "NCTU;\u4ea4\u5927;\u4ea4\u901a\u5927\u5b78;\u4ea4\u5927\u5149\u5fa9\u6821\u5340;\u4ea4\u901a\u5927\u5b78\u5149\u5fa9\u6821\u5340, 1001, \u5927\u5b78\u8def, \u5149\u660e\u91cc, \u8d64\u571f\u5d0e, \u6771\u5340, \u65b0\u7af9\u5e02, 30010, \u81fa\u7063", "24.78676765", "120.99724412", "edu", ""], ["A Two-Stage Approach to Robust Tensor Decomposition", "Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA", "33.58667840", "-101.87539204", "edu", ""], ["Local and Global Optimization Techniques in Graph-based Clustering", "The University of Tokyo, Japan", "University of Tokyo", "University of Tokyo", "\u6771\u4eac\u5927\u5b66 \u67cf\u30ad\u30e3\u30f3\u30d1\u30b9, \u5b66\u878d\u5408\u306e\u9053, \u67cf\u5e02, \u5343\u8449\u770c, \u95a2\u6771\u5730\u65b9, 277-8583, \u65e5\u672c", "35.90204480", "139.93622009", "edu", ""], ["Weighting Deep and Classic Representation via l 2 Regularization for Image Classification", "", "Jiangsu University", "Jiangsu University", "\u6c5f\u82cf\u5927\u5b66, 301, \u5b66\u5e9c\u8def, \u4eac\u53e3\u533a, \u8c61\u5c71\u8857\u9053, \u4eac\u53e3\u533a (Jingkou), \u9547\u6c5f\u5e02 / Zhenjiang, \u6c5f\u82cf\u7701, 212013, \u4e2d\u56fd", "32.20302965", "119.50968362", "edu", ""], ["Contrast compensation by fuzzy classification and image illumination analysis for back-lit and front-lit color face images", "Department of mechatronic technology of National Taiwan Normal University", "Taipei Municipal University of Education", "Taipei Municipal University of Education", "Taipei, Taiwan", "25.03296940", "121.56541770", "edu", ""], ["Multilevel Quadratic Variation Minimization for 3D Face Modeling and Virtual View Synthesis", "Griffith University", "Griffith University", "Griffith University", "Griffith University Nathan Campus, Johnson Path, Nathan, Nathan Heights, QLD, 4111, Australia", "-27.55339750", "153.05336234", "edu", ""], ["Face recognition using the classified appearance-based quotient image", "Corp. 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and Engineering, University of Electronic Science and Technology of China, Chengdu, People\u2019s Republic of China", "University of Electronic Science and Technology of China", "University of Electronic Science and Technology of China", "Columbus, OH 43210, USA", "40.01419050", "-83.03091430", "edu", ""], ["Boosting Kernel Discriminative Common Vectors for Face Recognition", "SRM University, Kattankulathur, Chennai-603 203, Tamilnadu, India", "Bharathidasan University", "Bharathidasan University", "Bharathidasan University Road, Kajamalai, Ponmalai, Ponmalai Zone, Tiruchchir\u0101ppalli, Tiruchchirappalli district, Tamil Nadu, 620020, India", "10.77788450", "78.69663190", "edu", ""], ["Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems", "", "University of Basel", "University of Basel", "Faculty of Psychology, University of Basel, 60-62, Missionsstrasse, Grossbasel, Am Ring, Basel, Basel-Stadt, 4055, Schweiz/Suisse/Svizzera/Svizra", "47.56126510", "7.57529610", "edu", ""], ["On intelligent surveillance systems and face recognition for mass transport security", "NICTA, PO Box 6020, St Lucia, QLD 4067, Australia", "CSIRO, Australia", "NICTA, PO Box 6020, St Lucia, QLD 4067, Australia", "Research Way, Clayton VIC 3168, Australia", "-37.90627370", "145.13194490", "edu", "f.k.a. 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Hsinchu 300, Taiwan", "National Tsing Hua University", "National Tsing Hua University", "\u570b\u7acb\u6e05\u83ef\u5927\u5b78, 101, \u514b\u606d\u6a4b, \u5149\u660e\u91cc, \u8d64\u571f\u5d0e, \u6771\u5340, \u65b0\u7af9\u5e02, 30013, \u81fa\u7063", "24.79254840", "120.99511830", "edu", ""], ["Mining discriminative components with low-rank and sparsity constraints for face recognition", "Arizona State University", "Arizona State University", "Arizona State University", "Arizona State University Polytechnic campus, East Texas Avenue, Mesa, Maricopa County, Arizona, 85212, USA", "33.30715065", "-111.67653157", "edu", ""], ["Model-based reconstruction for illumination variation in face images", "Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O.Box 217 7500 AE Enschede, The Netherlands", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Fast Approximate L_infty Minimization: Speeding Up Robust Regression", "School of Computer Science, The University of Adelaide, Australia", "University of Adelaide", "University of Adelaide", "University of Adelaide, North Terrace, Adelaide, 5000, City of Adelaide, South Australia, 5000, Australia", "-34.91892260", "138.60423668", "edu", ""], ["Toward Development of a Face Recognition System for Watchlist Surveillance", "Office of Naval Research, Arlington", "Office of Naval Research, Arlington", "Office of Naval Research, Arlington", "875 N Randolph St, Arlington, VA 22217, USA", "38.88079270", "-77.10869400", "mil", ""], ["Combining Classifier for Face Identification at Unknown Views with a Single Model Image", "HCI Lab., Samsung Advanced Institute of Technology, Yongin, Korea", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["The Indian Buffet Process: Scalable Inference and Extensions", "The University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Semi-Supervised Dimensionality Reduction", "Nanjing University, Nanjing 210093, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Face View Synthesis Using A Single Image", "c(cid:13) Carnegie Mellon University", "Robotics Institute", "Robotics Institute", "Institute for Field Robotics, \u0e1b\u0e23\u0e30\u0e0a\u0e32\u0e2d\u0e38\u0e17\u0e34\u0e28, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, 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University, Raleigh, NC, USA", "North Carolina State University", "North Carolina State University", "North Carolina State University, Oval Drive, West Raleigh, Raleigh, Wake County, North Carolina, 27695, USA", "35.77184965", "-78.67408695", "edu", ""], ["On averaging face images for recognition under pose variations", "Computer Vision and Image Processing Lab, Institute for Integrated and Intelligent Systems, Griffith University, Australia", "Griffith University", "Griffith University", "Griffith University Nathan Campus, Johnson Path, Nathan, Nathan Heights, QLD, 4111, Australia", "-27.55339750", "153.05336234", "edu", ""], ["Learning Multi-channel Deep Feature Representations for Face Recognition", "Wayne State University, Detroit, MI 48202, USA", "Wayne State University", "Wayne State University", "Parking Structure 3, East Warren Avenue, New Center, Detroit, Wayne County, Michigan, 48236, USA", "42.35775700", "-83.06286711", "edu", ""], ["SampleBoost: Improving boosting 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University", "Xi'an Jiaotong University", "\u897f\u5b89\u4ea4\u901a\u5927\u5b66\u5174\u5e86\u6821\u533a, \u6587\u6cbb\u8def, \u4e50\u5c45\u573a, \u7891\u6797\u533a (Beilin), \u897f\u5b89\u5e02, \u9655\u897f\u7701, 710048, \u4e2d\u56fd", "34.24749490", "108.97898751", "edu", ""], ["Robust and Practical Face Recognition via Structured Sparsity", "University of Illinois at Urbana-Champaign", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["A Framework of Joint Graph Embedding and Sparse Regression for Dimensionality Reduction", "Shenzhen Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["A deep discriminative and robust nonnegative matrix factorization network method with soft label constraint", "School of Electronic Engineering, Xidian University, Xi\u2019an, China", "Xidian University", "Xidian University", "Xidian University (New Campus), 266\u53f7, \u94f6\u674f\u5927\u9053, \u5357\u96f7\u6751, \u957f\u5b89\u533a (Chang'an), \u897f\u5b89\u5e02, \u9655\u897f\u7701, 710126, \u4e2d\u56fd", "34.12358250", "108.83546000", "edu", ""], ["Analysis of photometric factors based on photometric linearization.", "", "Okayama University", "Okayama University", "\u5ca1\u5c71\u5927\u5b66, \u6d25\u9ad8\u6cd5\u754c\u9662\u505c\u8eca\u5834\u7dda, \u6d25\u5cf6\u67712, \u6d25\u5cf6\u6771, \u5317\u533a, \u5ca1\u5c71\u5e02, \u5ca1\u5c71\u770c, \u4e2d\u56fd\u5730\u65b9, 700-0081, \u65e5\u672c", "34.68933930", "133.92222720", "edu", ""], ["Robust Principal Component Analysis with Complex Noise", "The Hong Kong Polytechnic University, Hong Kong, China", "Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "hong kong, 11, \u80b2\u624d\u9053 Yuk Choi Road, \u5c16\u6c99\u5480 Tsim Sha Tsui, \u6cb9\u5c16\u65fa\u5340 Yau Tsim Mong District, \u4e5d\u9f8d Kowloon, HK, 00000, \u4e2d\u56fd", "22.30457200", "114.17976285", "edu", ""], ["Greedy Feature Selection for Subspace Clustering Greedy Feature Selection for Subspace Clustering", "Rice University, Houston, TX, 77005, USA", "Rice University", "Rice University", "Rice University, Stockton Drive, Houston, Harris County, Texas, 77005-1890, USA", "29.71679145", "-95.40478113", "edu", ""], ["Is face recognition really a Compressive Sensing problem?", "The Australian Centre for Visual Technologies, The university of Adelaide", "University of Adelaide", "University of Adelaide", "University of Adelaide, North Terrace, Adelaide, 5000, City of Adelaide, South Australia, 5000, Australia", "-34.91892260", "138.60423668", "edu", ""], ["Similarity-Preserving Binary Signature for Linear Subspaces", "Tsinghua University, Beijing 100084, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["An illumination normalization model for face recognition under varied lighting conditions", "Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China", "Institute of Information Science", "Institute of Information Science", "\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240, \u6578\u7406\u5927\u9053, \u4e2d\u7814\u91cc, \u5357\u6e2f\u5b50, \u5357\u6e2f\u5340, \u81fa\u5317\u5e02, 11574, \u81fa\u7063", "25.04107280", "121.61475620", "edu", ""], ["Variational Inference for the Indian Buffet Process", "University of California, Berkeley", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["Online Face Recognition with Application to Proactive Augmented Reality", "AALTO UNIVERSITY", "Aalto University", "Aalto University", "Aalto, 24, Otakaari, Otaniemi, Suur-Tapiola, Espoo, Helsingin seutukunta, Uusimaa, Etel\u00e4-Suomi, Manner-Suomi, 02150, Suomi", "60.18558755", "24.82427330", "edu", ""], ["Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models", "Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180 USA", "Rensselaer Polytechnic Institute", "Rensselaer Polytechnic Institute", "Rensselaer Polytechnic Institute, Sage Avenue, Downtown, City of Troy, Rensselaer County, New York, 12180, USA", "42.72984590", "-73.67950216", "edu", ""], ["Recovering intrinsic images from image sequences using total variation models", "Shenzhen VisuCA Key Lab / SIAT, Chinese Academy of Sciences, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Learning scale ranges for the extraction of regions of interest", "Western Kentucky University", "Western Kentucky University", "Western Kentucky University", "Western Kentucky University, Avenue of Champions, Bowling Green, Warren County, Kentucky, 42101, USA", "36.98453170", "-86.45764430", "edu", ""], ["Illumination Normalization of Face Image Based on Illuminant Direction Estimation and Improved Retinex", "University POLITEHNICA Timisoara, Timisoara, 300223, Romania", "University Politehnica Timisoara", "University POLITEHNICA Timisoara", "UPT, Bulevardul Vasile P\u00e2rvan, Elisabetin, Timi\u0219oara, Timi\u0219, 300223, Rom\u00e2nia", "45.74618900", "21.22755075", "edu", ""], ["Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting", "National University of Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Robust Bayesian method for simultaneous block sparse signal recovery with applications to face recognition", "University of California, San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["Study on Parameter Selection Using SampleBoost", "University of North Texas, Denton, Texas, USA", "University of North Texas", "University of North Texas", "University of North Texas, West Highland Street, Denton, Denton County, Texas, 76201, USA", "33.20988790", "-97.15147488", "edu", ""], ["Multilayer Surface Albedo for Face Recognition With Reference Images in Bad Lighting Conditions", "Department of MathematicsIntelligent Data Center, Sun Yat-sen University, Guangzhou, China", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["Fast and compact Kronecker-structured dictionary learning for classification and representation", "Electrical and Computer Engineering, Wayne State University, Detroit, Michigan 48202", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA", "33.58667840", "-101.87539204", "edu", ""], ["Null space clustering with applications to motion segmentation and face clustering", "Australian National University, Canberra", "Australian National University, Canberra", "Australian National University, Canberra", "Australian National University, Garran Road, Acton, Canberra, Canberra Central, Australian Capital Territory, 2601, Australia", "-35.28121335", "149.11665331", "edu", ""], ["Face recognition using multi-modal low-rank dictionary learning", "University of Alberta, Edmonton, Canada", "University of Alberta", "University of Alberta", "University of Alberta, 87 Avenue NW, University of Alberta, Edmonton, Alberta, T6G, Canada", "53.52385720", "-113.52282665", "edu", ""], ["Online incremental clustering with distance metric learning for high dimensional data", "Dept.of Intelligence Science and Technology, The Kyoto University of JAPAN", "Kyoto University, Japan", "The Kyoto University of JAPAN", "Yoshidahonmachi, Sakyo Ward, Kyoto, Kyoto Prefecture 606-8501, Japan", "35.02624440", "135.78082180", "edu", ""], ["Learning Locality-Constrained Collaborative Representation for Face Recognition", "", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Face Recognition from Still Images and Videos", "University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Transferred Dimensionality Reduction", "Tsinghua University, Beijing 100084, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Expression-invariant three-dimensional face recognition", "Technion   Israel Institute of Technology", "Technion Israel Institute of Technology", "Technion   Israel Institute of Technology", "\u05d4\u05d8\u05db\u05e0\u05d9\u05d5\u05df - \u05de\u05db\u05d5\u05df \u05d8\u05db\u05e0\u05d5\u05dc\u05d5\u05d2\u05d9 \u05dc\u05d9\u05e9\u05e8\u05d0\u05dc, \u05d3\u05d5\u05d3 \u05e8\u05d5\u05d6, \u05d7\u05d9\u05e4\u05d4, \u05e7\u05e8\u05d9\u05ea \u05d4\u05d8\u05db\u05e0\u05d9\u05d5\u05df, \u05d7\u05d9\u05e4\u05d4, \u05de\u05d7\u05d5\u05d6 \u05d7\u05d9\u05e4\u05d4, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.77675360", "35.02414529", "edu", ""], ["Recognition from Appearance Subspaces across Image Sets of Variable Scale", "University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Face Illumination Manipulation Using a Single Reference Image by Adaptive Layer Decomposition", "State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Real-time face recognition for human-robot interaction", "Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Luis Enrique Erro #1, Tonantzintla, Puebla, Mexico", "Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Mexico", "Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Luis Enrique Erro #1, Tonantzintla, Puebla, Mexico", "Luis Enrique Erro 1, Sta Mar\u00eda Tonanzintla, 72840 Puebla, Pue., Mexico", "19.03231070", "-98.31537020", "edu", ""], ["Computational Explorations of Split Architecture in Modeling Face and Object Recognition", "University of California San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["Subspace Clustering using Ensembles of $K$-Subspaces", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Face recognition via non-negative sparse low-rank representation classification", "College of Information Engineering, Yangzhou University, Yangzhou, China", "Yangzhou University", "College of Information Engineering, Yangzhou University, Yangzhou, China", "196 Huayang W Rd, Hanjiang Qu, Yangzhou Shi, Jiangsu Sheng, China", "32.33934870", "119.39704100", "edu", ""], ["Low-Rank-Sparse Subspace Representation for Robust Regression", "The University of Sydney", "University of Sydney", "University of Sydney", "USyd, Fisher Road, Camperdown, Sydney, NSW, 2006, Australia", "-33.88890695", "151.18943366", "edu", ""], ["Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose", "Queensland Research Laboratory, National ICT Australia and Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Australia", "Griffith University", "Griffith University", "Griffith University Nathan Campus, Johnson Path, Nathan, Nathan Heights, QLD, 4111, Australia", "-27.55339750", "153.05336234", "edu", ""], ["Hierarchical Clustering in Face Similarity Score Space", "University of Notre Dame", "University of Notre Dame", "University of Notre Dame", "University of Notre Dame du Lac, Holy Cross Drive, Notre Dame, Maple Lane, Saint Joseph County, Indiana, 46556, USA", "41.70456775", "-86.23822026", "edu", ""], ["Performance Evaluation of Illumination Normalization Techniques for Face Recognition", "PSG College of Technology, Coimbatore, Tamil Nadu, India", "PSG College of Technology, Coimbatore, Tamil Nadu, India", "PSG College of Technology, Coimbatore, Tamil Nadu, India", "PSG College of Technology, Avinashi Road, Ward 38, North Zone, Coimbatore, Coimbatore district, Tamil Nadu, 641001, India", "11.02468330", "77.00284246", "edu", ""], ["Face Recognition Under Illumination Variation Using Shadow Compensation and Pixel Selection", "Dankook University, 126 Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do, Korea", "Dankook University", "Dankook University", "\ub2e8\uad6d\ub300\ud559\uad50 \uce58\uacfc\ubcd1\uc6d0, \uc8fd\uc804\ub85c, \uc8fd\uc804\ub3d9, \uc218\uc9c0\uad6c, \uc6a9\uc778\uc2dc, \uacbd\uae30, 16900, \ub300\ud55c\ubbfc\uad6d", "37.32195750", "127.12507230", "edu", ""], ["Robust Subspace Segmentation with Block-Diagonal Prior", "National University of Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Illuminated face normalization technique by using wavelet fusion and local binary patterns", "Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia", "Multimedia University", "Multimedia University", "Universiti Multimedia, Persiaran Neuron, Bandar Nusaputra, Cyberjaya, Selangor, 63000, Malaysia", "2.92749755", "101.64185301", "edu", ""], ["Face recognition under varying lighting conditions using self quotient image", "Inst. of Autom., Chinese Acad. of Sci., Beijing, China", "Chinese Academy of Science", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Beijing, China", "39.90419990", "116.40739630", "edu", ""], ["Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild", "Wayne State University", "Wayne State University", "Wayne State University", "Parking Structure 3, East Warren Avenue, New Center, Detroit, Wayne County, Michigan, 48236, USA", "42.35775700", "-83.06286711", "edu", ""], ["Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Face illumination normalization on large and small scale features", "School of Mathematics and Computational Science, Sun Yat-sen University, China", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["Illumination Face Spaces Are Idiosyncratic", "Colorado State University, Fort Collins, CO 80523, USA", "Colorado State University", "Colorado State University", "Colorado State University, West Pitkin Street, Woodwest, Fort Collins, Larimer County, Colorado, 80526-2002, USA", "40.57093580", "-105.08655256", "edu", ""], ["Scalable Sparse Subspace Clustering", "Machine Intelligence Laboratory, College of Computer Science, Sichuan University", "Sichuan University, Chengdu", "Sichuan Univ., Chengdu", "\u56db\u5ddd\u5927\u5b66\uff08\u534e\u897f\u6821\u533a\uff09, \u6821\u4e1c\u8def, \u6b66\u4faf\u533a, \u6b66\u4faf\u533a (Wuhou), \u6210\u90fd\u5e02 / Chengdu, \u56db\u5ddd\u7701, 610014, \u4e2d\u56fd", "30.64276900", "104.06751175", "edu", ""], ["Face recognition committee machine", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Improving Face Detection", "Faculty of Computer Science, University of A Coru na, Coru na, Spain", "University of Coimbra", "University of Coimbra", "Reitoria da Universidade de Coimbra, Rua de Entre-Col\u00e9gios, Almedina, Alta, Almedina, S\u00e9 Nova, Santa Cruz, Almedina e S\u00e3o Bartolomeu, CBR, Coimbra, Baixo Mondego, Centro, 3000-062, Portugal", "40.20759510", "-8.42566148", "edu", ""], ["Accelerated sampling for the Indian Buffet Process", "Cambridge University, Trumpington Street, Cambridge CB21PZ, UK", "Cambridge University", "Cambridge University", "University, Cambridge Road, Old Portsmouth, Portsmouth, South East, England, PO1 2HB, UK", "50.79440260", "-1.09717480", "edu", ""], ["Discriminative Lasso", "Software School, Xiamen University, Xiamen, China", "Xiamen University", "Xiamen University", "\u53a6\u95e8\u5927\u5b66, \u601d\u660e\u5357\u8def Siming South Road, \u601d\u660e\u533a, \u601d\u660e\u533a (Siming), \u53a6\u95e8\u5e02 / Xiamen, \u798f\u5efa\u7701, 361005, \u4e2d\u56fd", "24.43994190", "118.09301781", "edu", ""], ["Hierarchical multi-label framework for robust face recognition", "Computational Biomedicine Lab, Department of Computer Science, University of Houston, TX, USA", "University of Houston", "University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["Improving image clustering: An unsupervised feature weight learning framework", "IBM China Research Laboratory, Beijing, China", "IBM China Research Laboratory", "IBM China Research Laboratory, Beijing, China", "Beijing, China", "39.90419990", "116.40739630", "company", ""], ["Automatic Frequency Band Selection for Illumination Robust Face Recognition", "Institute of Anthropomatics, Karlsruhe Institute of Technology, Germany", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["A comparison of photometric normalisation algorithms for face verification", "University of Surrey", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Robust Low Rank Subspace Segmentation via Joint $$\\ell _{21} $$ \u211321 -Norm Minimization", "School of Science, Jiangnan University, Wuxi, China", "Jiangnan University", "Jiangnan University", "\u6c5f\u5357\u5927\u5b66\u7ad9, \u8821\u6e56\u5927\u9053, \u6ee8\u6e56\u533a, \u5357\u573a\u6751, \u6ee8\u6e56\u533a (Binhu), \u65e0\u9521\u5e02 / Wuxi, \u6c5f\u82cf\u7701, 214121, \u4e2d\u56fd", "31.48542550", "120.27395810", "edu", ""], ["Co-Regularized Ensemble for Feature Selection", "School of Information Technology and Electrical Engineering, The University of Queensland", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Learning the sparse representation for classification", "Beckman Institute, University of Illinois at Urbana-Champaign, USA", "Beckman Institute", "Beckman Institute", "Beckman Institute, The Presidents' Walk, Urbana, Champaign County, Illinois, 61801-2341, USA", "40.11571585", "-88.22750772", "edu", ""], ["<inline-formula> <tex-math notation=\"LaTeX\">$L_{1}$ </tex-math></inline-formula>-Minimization Algorithms for Sparse Signal Reconstruction Based on a Projection Neural Network", "School of Automation, Huazhong University of Science and Technology, Wuhan, China", "Huazhong University of Science and Technology", "Huazhong University of Science and Technology", "\u534e\u4e2d\u5927, \u73de\u55bb\u8def, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a, \u5173\u4e1c\u8857\u9053, 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Verification", "Microsoft Research, Redmond, WA", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Generating face images under multiple illuminations based on a single front-lighted sample without 3D models", "State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering, Tsinghua University, Beijing 100084, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["A second order polynomial based subspace projection method for dimensionality reduction", "ODU Vision Lab, Old Dominion University, Norfolk, VA, USA", "Old Dominion University", "Old Dominion University", "Old Dominion University, Elkhorn Avenue, Lamberts Point, Norfolk, Virginia, 23508, USA", "36.88568200", "-76.30768579", "edu", ""], ["Sparse eigentracker augmented by associative mapping to 3D shape", "Department of Computer Science, Okayama University, Okayama, Japan", "Okayama University", "Okayama University", "\u5ca1\u5c71\u5927\u5b66, \u6d25\u9ad8\u6cd5\u754c\u9662\u505c\u8eca\u5834\u7dda, \u6d25\u5cf6\u67712, \u6d25\u5cf6\u6771, \u5317\u533a, \u5ca1\u5c71\u5e02, \u5ca1\u5c71\u770c, \u4e2d\u56fd\u5730\u65b9, 700-0081, \u65e5\u672c", "34.68933930", "133.92222720", "edu", ""], ["Generalized Projection-Based M-Estimator", "Rutgers University, Piscataway", "Rutgers University", "Rutgers University", "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA", "40.47913175", "-74.43168868", "edu", ""], ["Kernel principal component analysis network for image classification", "Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing", 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Campus Drive, Burch Avenue, Durham, Durham County, North Carolina, 27705, USA", "35.99905220", "-78.92906290", "edu", ""], ["Video-Based Face Recognition Using Probabilistic Appearance Manifolds", "University of Illinois, Urbana-Champaign University of California, San Diego", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics", "Department of Computer Science, University of York, York, UK", "University of York", "University of York", "University of York, Lakeside Way, Heslington, York, Yorkshire and the Humber, England, YO10 5FN, UK", "53.94540365", "-1.03138878", "edu", ""], ["Structure-constrained low-rank and partial sparse representation for image classification", "School of Information and Communication Engineering, Beijing 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Asia, Beijing, China", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Face recognition using localized features based on non-negative sparse coding", "Centre for Intelligent Machines and Department of Electrical and Computer Engineering, McGill University, Montreal, Canada", "McGill University", "McGill University", "McGill University, Rue Sherbrooke Ouest, Quartier des Spectacles, Ville-Marie, Montr\u00e9al, Agglom\u00e9ration de Montr\u00e9al, Montr\u00e9al (06), Qu\u00e9bec, H3A 3P8, Canada", "45.50397610", "-73.57496870", "edu", ""], ["An efficient illumination invariant face recognition technique using two dimensional linear discriminant analysis", "Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India", "Indian Statistical Institute, Kolkata", "Indian Statistical Institute, Kolkata", "Plot No. 203, Barrackpore Trunk Road, Baranagar, Kolkata, West Bengal 700108, India", 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"Australian National University", "Australian National University", "Canberra ACT 0200, Australia", "-35.27769990", "149.11852700", "edu", ""], ["Image Preprocessing for Illumination Invariant Face Verification", "Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland", "Warsaw University of Technology", "Warsaw University of Technology", "Politechnika Warszawska, 1, Plac Politechniki, VIII, \u015ar\u00f3dmie\u015bcie, Warszawa, mazowieckie, 00-661, RP", "52.22165395", "21.00735776", "edu", ""], ["Implicit elastic matching with random projections for pose-variant face recognition", "University of Illinois at Urbana-Champaign", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Resource-Allocating Codebook for patch-based face recognition", "University of Southampton, SO17 1BJ, UK", "University of 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"School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["A collaborative face recognition framework on a social network platform", "School of Electrical & Electronic Engineering, Yonsei University, Seoul, South Korea, 120-749", "Yonsei University", "Yonsei University", "\uc5f0\uc138\ub300, \uc5f0\uc138\ub85c, \uc2e0\ucd0c\ub3d9, \ucc3d\ucc9c\ub3d9, \uc11c\ub300\ubb38\uad6c, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, 03789, \ub300\ud55c\ubbfc\uad6d", "37.56004060", "126.93692480", "edu", ""], ["Robust human face recognition based on locality preserving sparse over complete block approximation", "University of Geneva", "University of Geneva", "University of Geneva", "University of Chicago-Yerkes Observatory, 373, West Geneva Street, Williams Bay, 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Labs., NEC Corp., Kanagawa, Japan", "Kanagawa Prefecture, Japan", "35.44750730", "139.64234460", "company", ""], ["Locality Preserving Discriminant Projection", "Dhirubhai Ambani Institute of Information and Communication Technology, India", "Dhirubhai Ambani Institute of Information and Communication Technology, India", "Dhirubhai Ambani Institute of Information and Communication Technology, India", "Near Reliance Chowkdi, DA IICT Road, Gandhinagar, Gujarat 382007, India", "23.18854690", "72.62902940", "edu", ""], ["Local and holistic texture analysis approach for face recognition", "Department of Computer Engineering, Mahanakorn University of Technology, 140 Cheum-Sampan Rd., Nong Chok, Bangkok THAILAND 10530", "Mahanakorn University of Technology", "Mahanakorn University of Technology", "\u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22\u0e40\u0e17\u0e04\u0e42\u0e19\u0e42\u0e25\u0e22\u0e35\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, 140, \u0e16\u0e19\u0e19\u0e40\u0e0a\u0e37\u0e48\u0e2d\u0e21\u0e2a\u0e31\u0e21\u0e1e\u0e31\u0e19\u0e18\u0e4c, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, \u0e40\u0e02\u0e15\u0e2b\u0e19\u0e2d\u0e07\u0e08\u0e2d\u0e01, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, 10530, \u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e44\u0e17\u0e22", "13.84450465", "100.85620818", "edu", ""], ["From Gabor Magnitude to Gabor Phase Features: Tackling the Problem of Face Recognition under Severe Illumination Changes", "Faculty of Electrical Engineering, University of Ljubljana", "University of Ljubljana", "University of Ljubljana", "UL Fakulteta za ra\u010dunalni\u0161tvo in informatiko, 113, Ve\u010dna pot, Vrta\u010da, Ro\u017ena dolina, Ljubljana, Upravna Enota Ljubljana, Osrednjeslovenska, 1000, Slovenija", "46.05015580", "14.46907327", "edu", ""], ["Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques", "Ryerson Multimedia Research Laboratory, Ryerson University, Toronto, Ontario, Canada", "Ryerson University", "Ryerson University", "Ryerson University, Gould Street, Downtown Yonge, Old Toronto, Toronto, Ontario, M5B 2G9, Canada", "43.65815275", "-79.37908010", "edu", ""], ["Robust Face Recognition Technique under Varying Illumination", "", "National Autonomous University of Mexico", "Centro de Ciencias Aplicadas y Desarrollo Tecnol\u00f3gico", "University City, Mexico City, CDMX, Mexico", "19.31888950", "-99.18436760", "edu", "National Autonomous University of Mexico"], ["Evaluation of Face Datasets as Tools for Assessing the\u00a0Performance of Face Recognition Methods", "Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, USA", "National Institutes of Health", "National Institutes of Health", "NIH, Pooks Hill, Bethesda, Montgomery County, Maryland, USA", "39.00041165", "-77.10327775", "edu", ""], ["CapsNet comparative performance evaluation for image classification", "University of Waterloo, ON, Canada", "University of Waterloo", "University of Waterloo", "University of Waterloo, 200, University Avenue West, Northdale, Beechwood, Waterloo, Regional Municipality of Waterloo, Ontario, N2L 3G1, Canada", "43.47061295", "-80.54724732", "edu", ""], ["Multimedia Database Retrieval", "Electrical and Computer Engineering, Ryerson University, Toronto, Canada", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA", "33.58667840", "-101.87539204", "edu", ""], ["Discriminative Collaborative Representation for Classification", "Institute of Scienti c and Industrial Research, Osaka University, Ibaraki-shi 567-0047, Japan", "Osaka University", "Osaka University", "\u5927\u962a\u5927\u5b66\u6e05\u660e\u5bee, \u670d\u90e8\u897f\u753a\u56db\u4e01\u76ee, \u8c4a\u4e2d\u5e02, \u5927\u962a\u5e9c, \u8fd1\u757f\u5730\u65b9, \u65e5\u672c", "34.80809035", "135.45785218", "edu", ""], ["Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning", "School of Info. and Commu. 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Information Engineering, Beihang University, Beijing, China", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Face Recognition under Varying Illuminations Using Local Binary Pattern And Local Ternary Pattern Fusion", "Punjabi University Patiala", "Punjabi University Patiala", "Punjabi University Patiala", "Punjabi University Patiala, Rajpura Road, Patiala, Punjab, 147001, India", "30.35689810", "76.45512720", "edu", ""], ["Multiple feature fusion by subspace learning", "University of Illinois at", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Discriminative Dictionary Learning with Pairwise Constraints", "University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Generalised Scalable Robust Principal Component Analysis", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification", "Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China", "Xidian University", "Xidian University", "Xidian University (New Campus), 266\u53f7, \u94f6\u674f\u5927\u9053, \u5357\u96f7\u6751, \u957f\u5b89\u533a (Chang'an), \u897f\u5b89\u5e02, \u9655\u897f\u7701, 710126, \u4e2d\u56fd", "34.12358250", "108.83546000", "edu", ""], ["Collaborative Low-Rank Subspace Clustering", "bDiscipline of Business Analytics, The University of Sydney Business School", "Charles Sturt University", "Charles Sturt University", "Charles Sturt University, Wagga Wagga, NSW, 2678, Australia", "-35.06360710", "147.35522340", "edu", ""], ["Attribute and simile classifiers for face verification", "Columbia University", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["Describable Visual Attributes for Face Images", "COLUMBIA UNIVERSITY", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["Interpolation Between Eigenspaces Using Rotation in Multiple Dimensions", "Japan", "Nagoya University", "Nagoya University", "SuperDARN (Hokkaido West), \u592a\u8f9b\u7b2c1\u652f\u7dda\u6797\u9053, \u9678\u5225\u753a, \u8db3\u5bc4\u90e1, \u5341\u52dd\u7dcf\u5408\u632f\u8208\u5c40, \u5317\u6d77\u9053, \u5317\u6d77\u9053\u5730\u65b9, \u65e5\u672c", "43.53750985", "143.60768225", "edu", ""], ["Face recognition committee machines: dynamic vs. static structures", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Implementation of a New Methodology to Reduce the Effects of Changes of Illumination in Face Recognition-based Authentication", "Howard University, Washington DC", "Howard University", "Howard University", "Howard University, College Street Northwest, Howard University, Washington, D.C., 20001, USA", "38.92152500", "-77.01953566", "edu", ""], ["Standardization of Face Image Sample Quality", "University of Science and Technology of China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Subspace Methods", "", "University of Tsukuba", "University of Tsukuba", "University of Tsukuba, \u3064\u3070\u304d\u901a\u308a, Kananemoto-satsukabe village, \u3064\u304f\u3070\u5e02, \u8328\u57ce\u770c, \u95a2\u6771\u5730\u65b9, 305-8377, \u65e5\u672c", "36.11120580", "140.10551760", "edu", ""], ["Large-Cone Nonnegative Matrix Factorization", "Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia", "University of Technology Sydney", "University of Technology Sydney", "University of Technology Sydney, Omnibus Lane, Ultimo, Sydney, NSW, 2007, Australia", "-33.88096510", "151.20107299", "edu", ""], ["Image-based face recognition under illumination and pose variations.", "", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search", "", "Tongji University", "Tongji University", "\u540c\u6d4e\u5927\u5b66, 1239, \u56db\u5e73\u8def, \u6c5f\u6e7e, \u8679\u53e3\u533a, \u4e0a\u6d77\u5e02, 200092, \u4e2d\u56fd", "31.28473925", "121.49694909", "edu", ""], ["1 Face Recognition by Sparse Representation", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Locality-Constrained Low-Rank Coding for Image Classification", "", "National University of Defense Technology, China", "National University of Defence Technology, Changsha 410000, China", "\u56fd\u9632\u79d1\u5b66\u6280\u672f\u5927\u5b66, \u4e09\u4e00\u5927\u9053, \u5f00\u798f\u533a, \u5f00\u798f\u533a (Kaifu), \u957f\u6c99\u5e02 / Changsha, \u6e56\u5357\u7701, 410073, \u4e2d\u56fd", "28.22902090", "112.99483204", "edu", ""], ["L1-(2D)2PCANet: A Deep Learning Network for Face Recognition", "", "Jiangnan University", "Jiangnan University", "\u6c5f\u5357\u5927\u5b66\u7ad9, \u8821\u6e56\u5927\u9053, \u6ee8\u6e56\u533a, \u5357\u573a\u6751, \u6ee8\u6e56\u533a (Binhu), \u65e0\u9521\u5e02 / Wuxi, \u6c5f\u82cf\u7701, 214121, \u4e2d\u56fd", "31.48542550", 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"-1.89433739", "edu", ""], ["Recovering 3-D Shape and Reflectance From a Small Number of Photographs", "", "Yale University", "Yale University", "Yale University, West Campus Drive, West Haven, New Haven County, Connecticut, 06516, USA", "41.25713055", "-72.98966960", "edu", ""], ["Robust Face Recognition via Sparse Representation", "", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["Probabilistic Subspace Clustering Via Sparse Representations", "", "Technion", "Technion", "Haifa, 3200003, Israel", "32.77677830", "35.02312710", "edu", ""], ["Classification via Minimum Incremental Coding Length (MICL)", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", 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Korea", "Samsung SAIT, Korea", "Advanced Media Lab. Samsung Advance Institute of Technology, Republic of Korea", "130 Samseong-ro, Maetan 3(sam)-dong, Yeongtong-gu, Suwon, Gyeonggi-do, South Korea", "37.25202260", "127.05550190", "company", ""], ["A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction", "Colorado State University", "Colorado State University", "Colorado State University", "Colorado State University, West Pitkin Street, Woodwest, Fort Collins, Larimer County, Colorado, 80526-2002, USA", "40.57093580", "-105.08655256", "edu", ""]]}
\ No newline at end of file
diff --git a/site/datasets/citations/27a2fad58dd8727e280f97036e0d2bc55ef5424c.json b/site/datasets/citations/27a2fad58dd8727e280f97036e0d2bc55ef5424c.json
index 7de56f0a..4ca3236d 100644
--- a/site/datasets/citations/27a2fad58dd8727e280f97036e0d2bc55ef5424c.json
+++ b/site/datasets/citations/27a2fad58dd8727e280f97036e0d2bc55ef5424c.json
@@ -1 +1 @@
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University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", ""], ["Towards a Principled Integration of Multi-camera Re-identification and Tracking Through Optimal Bayes Filters", "", "RWTH Aachen University", "RWTH Aachen University", "RWTH Aachen, Mies-van-der-Rohe-Stra\u00dfe, K\u00f6nigsh\u00fcgel, Aachen-Mitte, Aachen, St\u00e4dteregion Aachen, Regierungsbezirk K\u00f6ln, Nordrhein-Westfalen, 52074, Deutschland", "50.77917030", "6.06728733", "edu", ""], ["Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification", "", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Fusion of Head and Full-Body Detectors for Multi-Object Tracking", "", "Technical University Munich", "Technical University Munich", "TUM, 21, Arcisstra\u00dfe, Bezirksteil K\u00f6nigsplatz, Stadtbezirk 03 Maxvorstadt, M\u00fcnchen, Obb, Bayern, 80333, Deutschland", "48.14955455", "11.56775314", "edu", ""], ["A Dataset for Persistent Multi-target Multi-camera Tracking in RGB-D", "", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", ""], ["Multi-Level Factorisation Net for Person Re-Identification", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["End-to-End Deep Kronecker-Product Matching for Person Re-identification", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 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Technology of China", "University of Electronic Science and Technology of China", "Columbus, OH 43210, USA", "40.01419050", "-83.03091430", "edu", ""], ["Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification", "", "Charles Sturt University", "Charles Sturt University", "Charles Sturt University, Wagga Wagga, NSW, 2678, Australia", "-35.06360710", "147.35522340", "edu", ""], ["Person Transfer GAN to Bridge Domain Gap for Person Re-Identification", "", "University of Texas at San Antonio", "University of Texas at San Antonio", "UTSA, Paseo Principal, San Antonio, Bexar County, Texas, 78249-1620, USA", "29.58333105", "-98.61944505", "edu", ""], ["Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification", "", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", 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\ No newline at end of file
diff --git a/site/datasets/citations/28312c3a47c1be3a67365700744d3d6665b86f22.json b/site/datasets/citations/28312c3a47c1be3a67365700744d3d6665b86f22.json
new file mode 100644
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Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition", "The University of Queensland, School of ITEE, QLD 4072, Australia", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Standardization of Face Image Sample Quality", "University of Science and Technology of China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Combining Face and Iris 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CAS, No.6 Kexueyuan South Road, Beijing, 100080, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["A deep pyramid Deformable Part Model for face detection", "University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Comparison of Visible, Thermal Infra-Red and Range Images for Face Recognition", "", "University of Western Australia", "University of Western Australia", "UWA, 35, Underwood Avenue, Daglish, Perth, Western Australia, 6009, Australia", "-31.95040445", "115.79790037", "edu", ""], ["Scalable face image retrieval integrating multi-feature quantization and constrained reference re-ranking", "State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Robust Low-Rank Regularized Regression for Face Recognition with Occlusion", "", "Peking University", "Peking University", "\u5317\u4eac\u5927\u5b66, 5\u53f7, \u9890\u548c\u56ed\u8def, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100871, \u4e2d\u56fd", "39.99223790", "116.30393816", "edu", ""], ["Robust Face Recognition using Wavelet and DCT based Lighting Normalization, and Shifting-mean LDA", "", "Kyoto University", "Kyoto University", "\u4eac\u90fd\u5927\u5b66, \u4eca\u51fa\u5ddd\u901a, \u5409\u7530\u6cc9\u6bbf\u753a, \u5de6\u4eac\u533a, \u4eac\u90fd\u5e02, \u4eac\u90fd\u5e9c, \u8fd1\u757f\u5730\u65b9, 606-8501, \u65e5\u672c", "35.02749960", "135.78154513", "edu", ""], ["Fuzzy 3D Face Ethnicity Categorization", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Joint Object Class Sequencing and Trajectory Triangulation (JOST)", "The University of North Carolina, Chapel Hill", "University of North Carolina", "University of North Carolina", "University of North Carolina, Emergency Room Drive, Chapel Hill, Orange County, North Carolina, 27599, USA", "35.90503535", "-79.04775327", "edu", ""], ["An Automatic Hybrid Segmentation Approach for Aligned Face Portrait Images", "", "Graz University of Technology", "Graz University of Technology", "TU Graz, Inffeldgasse, Harmsdorf, Jakomini, Graz, Steiermark, 8010, \u00d6sterreich", "47.05821000", "15.46019568", "edu", ""], ["Dictionaries for image and video-based face recognition [Invited].", "National Institute of Standards and Technology, Gaithersburg, MD 20899, USA", "National Institute of Standards and Technology", "National Institute of Standards and Technology", "National Institute of Standards and Technology, Summer Walk Drive, Diamond Farms, Gaithersburg, Montgomery County, Maryland, 20878, USA", "39.12549380", "-77.22293475", "edu", ""], ["Automatic Facial Feature Extraction for Face Recognition", "", "Universit\u00e0 degli Studi di Milano", "Universit\u00e0 degli Studi di Milano", "Universit\u00e0 degli Studi di Milano, Via Camillo Golgi, Citt\u00e0 Studi, Milano, MI, LOM, 20133, Italia", "45.47567215", "9.23336232", "edu", ""], ["3D Facial Expression Recognition Based on Primitive Surface Feature Distribution", "", "SUNY Binghamton", "State University of New York at Binghamton", "State University of New York at Binghamton, East Drive, Hinman, Willow Point, Vestal Town, Broome County, New York, 13790, USA", "42.08779975", "-75.97066066", "edu", ""], ["Face Identi cation in Multimedia Archives", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Machine Analysis of Facial Expressions", "to visually detectable changes in facial appearance, including blushing and tears. These", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence", "Cyprus University of Technology", "Cyprus University of Technology", "Cyprus University of Technology", "Mitropoli Building - Cyprus University of Technology, Anexartisias, Limasol - \u039b\u03b5\u03bc\u03b5\u03c3\u03cc\u03c2, Limassol - \u039b\u03b5\u03bc\u03b5\u03c3\u03cc\u03c2, \u039a\u03cd\u03c0\u03c1\u03bf\u03c2 - K\u0131br\u0131s, 3036, \u039a\u03cd\u03c0\u03c1\u03bf\u03c2 - K\u0131br\u0131s", "34.67567405", "33.04577648", "edu", ""], ["Local Directional Pattern (LDP) for face recognition", "", "Kyung Hee University", "Kyung Hee University", "Kyung Hee Tae Kwon Do, Vons 2370 Truck Service Ramp, University City, San Diego, San Diego County, California, 92122, USA", "32.85363330", "-117.20352860", "edu", ""], ["A Study on Illumination Invariant Face Recognition Methods Based on Multiple Eigenspaces", "North Dakota State University, Fargo, ND58105, USA", "North Dakota State University", "North Dakota State University", "North Dakota State University, 15th Avenue North, Fargo, Cass County, North Dakota, 58102, USA", "46.89715500", "-96.81827603", "edu", ""], ["Analysis of Facial Images across Age Progression by Humans", "e ects of di erence factors, including age group, age gap", "West Virginia University", "West Virginia University", "88, Windsor Avenue, The Flatts, Morgantown, Monongalia County, West Virginia, 26505, USA", "39.65404635", "-79.96475355", "edu", ""], ["Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition", "Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Interdisciplinary Graduate School, SingaporeSingapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Unconstrained Face Recognition From a Single Image", "Siemens Corporate Research, 755 College Road East, Princeton, NJ", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Automatic analysis of malaria infected red blood cell digitized microscope images", "University College London", "University College London", "University College London", "UCL Institute of Education, 20, Bedford Way, Holborn, Bloomsbury, London Borough of Camden, London, Greater London, England, WC1H 0AL, UK", "51.52316070", "-0.12820370", "edu", ""], ["Client-specific A-stack model for adult face verification across aging", "School of Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Face Recognition with Histograms of Oriented Gradients", "", "Robotics Institute", "Robotics Institute", "Institute for Field Robotics, \u0e1b\u0e23\u0e30\u0e0a\u0e32\u0e2d\u0e38\u0e17\u0e34\u0e28, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, \u0e40\u0e02\u0e15\u0e23\u0e32\u0e29\u0e0e\u0e23\u0e4c\u0e1a\u0e39\u0e23\u0e13\u0e30, \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e21\u0e2b\u0e32\u0e19\u0e04\u0e23, 10140, \u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e44\u0e17\u0e22", "13.65450525", "100.49423171", "edu", ""], ["Demo: Robust face recognition via sparse representation", "Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Autoassociative Pyramidal Neural Network for face verification", "Centre of Informatics, Federal University of Pernambuco, Recife-PE, Brazil. Bruno J. T. Fernandes is also with the Polytechnic School, University of Pernambuco, Brazil", "University of Pernambuco", "University of Pernambuco, Recife-PE, Brazil", "Av. Gov. Agamenon Magalh\u00e3es - Santo Amaro, Recife - PE, 50100-010, Brazil", "-8.04406030", "-34.88611670", "edu", ""], ["Near infrared face recognition: A literature survey", "", "Islamic Azad University", "Islamic Azad University", "\u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u0622\u0632\u0627\u062f \u0627\u0633\u0644\u0627\u0645\u06cc, \u0647\u0645\u062f\u0627\u0646, \u0628\u062e\u0634 \u0645\u0631\u06a9\u0632\u06cc \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0627\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u200f\u0627\u06cc\u0631\u0627\u0646\u200e", "34.84529990", "48.55962120", "edu", ""], ["Cross-age face verification by coordinating with cross-face age verification", "Temple University, Philadelphia, USA", "Temple University", "Temple University", "Temple University School of Podiatric Medicine, Race Street, Chinatown, Philadelphia, Philadelphia County, Pennsylvania, 19103, USA", "39.95472495", "-75.15346905", "edu", ""], ["Seeing the face through the eyes: a developmental perspective on face expertise.", "", "University of London", "University of London", "Birkbeck College, Malet Street, Holborn, Bloomsbury, London Borough of Camden, London, Greater London, England, WC1E 7HX, UK", "51.52176680", "-0.13019072", "edu", ""], ["Learning scale ranges for the extraction of regions of interest", "Western Kentucky University", "Western Kentucky University", "Western Kentucky University", "Western Kentucky University, Avenue of Champions, Bowling Green, Warren County, Kentucky, 42101, USA", "36.98453170", "-86.45764430", "edu", ""], ["An Improved Illumination Normalization Approach based on Wavelet Tranform for Face Recognition from Single Training Image Per Person", "", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face", "", "Facebook", "Facebook", "250 Bryant St, Mountain View, CA 94041, USA", "37.39367170", "-122.08072620", "company", "Facebook, Mountain View, CA"], ["Face Recognition from Video Using the Generic Shape-Illumination Manifold", "University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Egocentric Video Biometrics", "", "Hebrew University of Jerusalem", "The Hebrew University of Jerusalem", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 \u05d4\u05e2\u05d1\u05e8\u05d9\u05ea \u05d1\u05d9\u05e8\u05d5\u05e9\u05dc\u05d9\u05dd, Reagan Plaza, \u05e7\u05e8\u05d9\u05ea \u05de\u05e0\u05d7\u05dd \u05d1\u05d2\u05d9\u05df, \u05d4\u05e8 \u05d4\u05e6\u05d5\u05e4\u05d9\u05dd, \u05d9\u05e8\u05d5\u05e9\u05dc\u05d9\u05dd, \u05de\u05d7\u05d5\u05d6 \u05d9\u05e8\u05d5\u05e9\u05dc\u05d9\u05dd, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "31.79185550", "35.24472300", "edu", ""], ["Efficient 3D Face Recognition with Gabor Patched Spectral Regression", "", "Beijing Jiaotong University", "Beijing Jiaotong University", "\u5317\u4eac\u4ea4\u901a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100044, \u4e2d\u56fd", "39.94976005", "116.33629046", "edu", ""], ["Machine Understanding of Human Behavior", "", "Beckman Institute", "Beckman Institute", "Beckman Institute, The Presidents' Walk, Urbana, Champaign County, Illinois, 61801-2341, USA", "40.11571585", "-88.22750772", "edu", ""], ["UNIVERSITY OF CALGARY A MULTIMODAL BIOMETRIC SYSTEM BASED ON RANK LEVEL FUSION by MD. MARUF MONWAR A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY", "", "University of Calgary", "University of Calgary", "University of Calgary, Service Tunnel, University Heights, Calgary, Alberta, T2N 1N7, Canada", "51.07840380", "-114.12870770", "edu", ""], ["Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions", "Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada", "University of British Columbia", "University of British Columbia", "University of British Columbia, Eagles Drive, Hawthorn Place, University Endowment Lands, Metro Vancouver, British Columbia, V6T, Canada", "49.25839375", "-123.24658161", "edu", ""], ["Naming every individual in news video monologues", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition", "", "Harbin Institute of Technology", "Harbin Institute of Technology", "\u54c8\u5c14\u6ee8\u5de5\u4e1a\u5927\u5b66, \u53f8\u4ee4\u8857, \u5357\u5c97\u533a, \u54c8\u5c14\u6ee8\u5e02 / Harbin, \u9ed1\u9f99\u6c5f\u7701, 150000, \u4e2d\u56fd", "45.74139210", "126.62552755", "edu", ""], ["Imputing human descriptions in semantic biometrics", "", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", ""], ["Unconstrained face verification using deep CNN features", "University of Maryland, College Park", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Robust Metric and Alignment for Profile-Based Face Recognition: An Experimental Comparison", "", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", "University of Washington", "University of Washington", "University of Washington", "University of Washington, Rainier Vista, Montlake, University District, Seattle, King County, Washington, 98195, USA", "47.65432380", "-122.30800894", "edu", ""], ["Face Recognition: Holistic Approaches an Analytical Survey", "", "COMSATS Institute of Information Technology, Lahore", "COMSATS Institute of Information Technology", "COMSATS Institute of Information Technology, Ali Akbar Road, Dawood Residency, \u0628\u062d\u0631\u06cc\u06c1 \u0679\u0627\u0624\u0646\u202c\u200e, Lahore District, \u067e\u0646\u062c\u0627\u0628, 54700, \u200f\u067e\u0627\u06a9\u0633\u062a\u0627\u0646\u200e", "31.40063320", "74.21372960", "edu", ""], ["On Recognizing Faces in Videos Using Clustering-Based Re-Ranking and Fusion", "Indraprastha Institute of Information Technology Delhi, New Delhi, India", "Indraprastha Institute of Information Technology Delhi, India", "Indraprastha Institute of Information Technology Delhi, Delhi, India", "Okhla Industrial Estate, Phase III, Near Govind Puri Metro Station, New Delhi, Delhi 110020, India", "28.54562820", "77.27315050", "edu", ""], ["Robust 3D Face Recognition Using Learned Visual Codebook", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Subspace Approximation of Face Recognition Algorithms: An Empirical Study", "", "National Institute of Standards and Technology", "National Institute of Standards and Technology", "National Institute of Standards and Technology, Summer Walk Drive, Diamond Farms, Gaithersburg, Montgomery County, Maryland, 20878, USA", "39.12549380", "-77.22293475", "edu", ""], ["Class specific dictionary learning for face recognition", "School of Computer Science Carnegie Mellon University Pittsburgh, PA, 15213, USA", "Purdue University", "Purdue University", "Purdue University, West Stadium Avenue, West Lafayette, Tippecanoe County, Indiana, 47907, USA", "40.43197220", "-86.92389368", "edu", ""], ["Face Alignment With Deep Regression", "School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China", "Huazhong University of Science and Technology", "Huazhong University of Science and Technology", "\u534e\u4e2d\u5927, \u73de\u55bb\u8def, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a, \u5173\u4e1c\u8857\u9053, \u4e1c\u6e56\u65b0\u6280\u672f\u5f00\u53d1\u533a\uff08\u6258\u7ba1\uff09, \u6d2a\u5c71\u533a (Hongshan), \u6b66\u6c49\u5e02, \u6e56\u5317\u7701, 430074, \u4e2d\u56fd", "30.50975370", "114.40628810", "edu", ""], ["Simulation of face recognition at a distance by scaling down images", "Dept. of Computer Science and Computer Engineering, University of Louisville, KY, USA", "University of Louisville", "University of Louisville", "University of Louisville, South Brook Street, Louisville, Jefferson County, Kentucky, 40208, USA", "38.21675650", "-85.75725023", "edu", ""], ["Facial Descriptors for Identity-Preserving Multiple People Tracking", "Swiss Federal Institute of Technology, Lausanne (EPFL", "ETH Z\u00fcrich", "ETH Z\u00fcrich", "ETH Z\u00fcrich, 101, R\u00e4mistrasse, Hochschulen, Altstadt, Z\u00fcrich, Bezirk Z\u00fcrich, Z\u00fcrich, 8092, Schweiz/Suisse/Svizzera/Svizra", "47.37645340", "8.54770931", "edu", 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United Kingdom", "University of St Andrews", "University of St Andrews", "University of St Andrews, North Street, Albany Park Student accommodation, Carngour, St Andrews, Fife, Scotland, KY16 9AJ, UK", "56.34119840", "-2.79309380", "edu", ""], ["Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns", "University of Houston", "University of Houston", "University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["What Do You Do? 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new file mode 100644
index 00000000..4cd294f6
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diff --git a/site/datasets/citations/2a171f8d14b6b8735001a11c217af9587d095848.json b/site/datasets/citations/2a171f8d14b6b8735001a11c217af9587d095848.json
index 0c679063..3d45ad5b 100644
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diff --git a/site/datasets/citations/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.json b/site/datasets/citations/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.json
new file mode 100644
index 00000000..ea196c8e
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+++ b/site/datasets/citations/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.json
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diff --git a/site/datasets/citations/2d3482dcff69c7417c7b933f22de606a0e8e42d4.json b/site/datasets/citations/2d3482dcff69c7417c7b933f22de606a0e8e42d4.json
new file mode 100644
index 00000000..e52e05ea
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+++ b/site/datasets/citations/2d3482dcff69c7417c7b933f22de606a0e8e42d4.json
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\ No newline at end of file
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new file mode 100644
index 00000000..f0252e03
--- /dev/null
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diff --git a/site/datasets/citations/370b5757a5379b15e30d619e4d3fb9e8e13f3256.json b/site/datasets/citations/370b5757a5379b15e30d619e4d3fb9e8e13f3256.json
index 96af9139..3d1d5382 100644
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Sign Language (ASL)", "Rutgers University, Computer and Information Sciences, 110 Frelinghuysen Road, Piscataway, NJ", "Rutgers University", "Rutgers University", "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA", "40.47913175", "-74.43168868", "edu", ""], ["Deep nonlinear metric learning with independent subspace analysis for face verification", "Institute of Automation, Chinese Academy of Sciences, Beijing, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Face recognition based on convolution siamese networks", "The Institute of Optics and Electronics Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Chengdu, China", "University of the Chinese Academy of Sciences", "The Institute of Optics and Electronics Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Chengdu, China", "Chengdu, Sichuan, China", "30.57281500", "104.06680100", "edu", ""], ["Automatic Facial Image Annotation and Retrieval by Integrating Voice Label and Visual Appearance", "National Taiwan University, Taipei, Taiwan Roc", "National Taiwan University", "National Taiwan University", "\u81fa\u5927;\u53f0\u5927, 1, \u7f85\u65af\u798f\u8def\u56db\u6bb5, \u5b78\u5e9c\u91cc, \u5927\u5b89\u5340, \u81fa\u5317\u5e02, 10617, \u81fa\u7063", "25.01682835", "121.53846924", "edu", ""], ["How Related Exemplars Help Complex Event Detection in Web Videos?", "School of Computer Science, Carnegie Mellon University, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Weighted sparse representation using a learned distance metric for face recognition", "Department of Information Management and Security, Korea University", "Korea University", "Korea University", "\uace0\ub824\ub300, \uc548\uc554\ub85c, \uc81c\uae30\ub3d9, \ub3d9\ub300\ubb38\uad6c, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, 02796, \ub300\ud55c\ubbfc\uad6d", "37.59014110", "127.03623180", "edu", ""], ["Personalized Age Progression with Aging Dictionary", "School of Computer Science and Engineering, Nanjing University of Science and Technology", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Person Identity Label Propagation in Stereo Videos", "Aristotle University of Thessaloniki", "Aristotle University of Thessaloniki", "Aristotle University of Thessaloniki", "\u0391\u03c1\u03b9\u03c3\u03c4\u03bf\u03c4\u03ad\u03bb\u03b5\u03b9\u03bf 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of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["A feature subtraction method for image based kinship verification under uncontrolled environments", "Department of Electronic Systems, Aalborg University, Denmark", "Aalborg University", "Aalborg University", "AAU, Pontoppidanstr\u00e6de, S\u00f8nder Tranders, Aalborg, Aalborg Kommune, Region Nordjylland, 9220, Danmark", "57.01590275", "9.97532827", "edu", ""], ["Rectified Linear Units Improve Restricted Boltzmann Machines", "University of Toronto, Toronto, ON M5S 2G4, Canada", "University of Toronto", "University of Toronto", "University of Toronto, St. George Street, Bloor Street Culture Corridor, Old Toronto, Toronto, Ontario, M5S 1A5, Canada", "43.66333345", "-79.39769975", "edu", ""], ["Face Synthesis for Eyeglass-Robust Face Recognition", "University of Chinese Academy of Sciences, Beijing, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Cosine Similarity Metric Learning for Face Verification", "School of Computer Science, University of Nottingham", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Discrimination-aware Channel Pruning for Deep Neural Networks", "", "University of Texas at Arlington", "University of Texas at Arlington", "University of Texas at Arlington, South Nedderman Drive, Arlington, Tarrant County, Texas, 76010, USA", "32.72836830", "-97.11201835", "edu", ""], ["How interesting images are: An atypicality approach for social networks", "Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, HI, 96822", "University of Hawaii", "University of Hawaii", "University of Hawaii at Manoa, Bachman Place, Lower M\u0101noa, Moiliili, Honolulu, Honolulu County, Hawaii, 96848, USA", "21.29827950", "-157.81869230", "edu", ""], ["How Important Is Weight Symmetry in Backpropagation?", "Center for Brains, Minds and Machines, McGovern Institute, MIT", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research (MIT), Main Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.36262950", "-71.09144810", "edu", ""], ["LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation", "Georgia Institute of Technology", "Georgia Institute of Technology", "Georgia Institute of Technology", "Georgia Tech, Atlantic Drive Northwest, Bellwood, Rockdale, Atlanta, Fulton County, Georgia, 30318, USA", "33.77603300", "-84.39884086", "edu", ""], ["Unconstrained human identification using comparative facial soft biometrics", "University of Southampton", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", ""], ["A Robust and Efficient Doubly Regularized Metric Learning Approach", "CISE, University of Florida, Gainesville, FL", "University of Florida", "University of Florida", "University of Florida, Southwest 16th Avenue, Diamond Village Apartments, City of Gainesville Municipal Boundaries, Alachua County, Florida, 32611, USA", "29.63287840", "-82.34901330", "edu", ""], ["Gender Classification by LUT Based Boosting of Overlapping Block Patterns", "Tampere University of Technology, Tampere, Finland", "Tampere University of Technology", "Tampere University of Technology", "TTY, 10, Korkeakoulunkatu, Finninm\u00e4ki, Hervanta, Tampere, Tampereen seutukunta, Pirkanmaa, L\u00e4nsi- ja Sis\u00e4-Suomen aluehallintovirasto, L\u00e4nsi-Suomi, Manner-Suomi, 33720, Suomi", "61.44964205", "23.85877462", "edu", ""], ["Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification", "North China University of Technology, Beijing 100144 CHINA", "Beijing Institute of Technology", "Beijing Institute of Technology University", "\u5317\u4eac\u7406\u5de5\u5927\u5b66, 5, \u4e2d\u5173\u6751\u5357\u5927\u8857, \u4e2d\u5173\u6751, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100872, \u4e2d\u56fd", "39.95866520", "116.30971281", "edu", ""], ["Rendering or normalization? An analysis of the 3D-aided pose-invariant face recognition", "University of Houston, Houston, TX, USA", "University of Houston", "University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["Computer Vision for Head Pose Estimation: Review of a Competition", "University of Paderborn, Germany", "Tampere University of Technology", "Tampere University of Technology", "TTY, 10, Korkeakoulunkatu, Finninm\u00e4ki, Hervanta, Tampere, Tampereen seutukunta, Pirkanmaa, L\u00e4nsi- ja Sis\u00e4-Suomen aluehallintovirasto, L\u00e4nsi-Suomi, Manner-Suomi, 33720, Suomi", "61.44964205", "23.85877462", "edu", ""], ["The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", "University of Washington", "University of Washington", "University of Washington", "University of Washington, Rainier Vista, Montlake, University District, Seattle, King County, Washington, 98195, USA", "47.65432380", "-122.30800894", "edu", ""], ["Deep or Shallow Facial Descriptors? A Case for Facial Attribute Classification and Face Retrieval", "Multimedia University, Cyberjaya, Malaysia", "Multimedia University", "Multimedia University", "Universiti Multimedia, Persiaran Neuron, Bandar Nusaputra, Cyberjaya, Selangor, 63000, Malaysia", "2.92749755", "101.64185301", "edu", ""], ["Storages Are Not Forever", "School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Learning Rotation-Invariant Local Binary Descriptor", "Department of Automation, State Key Lab of Intelligent Technologies and Systems, and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, 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University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Partially Shared MultiTask Convolutional Neural Network with Local Constraint for Face Attribute Learning", "Zhejiang University, China", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["Detecting person presence in TV shows with linguistic and structural features", "France Telecom - Orange Labs, Lannion, France", "Orange Labs, Lannion, France", "France Telecom - Orange Labs, Lannion, France", "2 Avenue Pierre Marzin, 22300 Lannion, France", "48.75416800", "-3.45845860", "company", ""], ["BUAA-iCC at ImageCLEF 2015 Scalable Concept Image Annotation Challenge", "School of Information Technology and Management, University of International", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework", "", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Face Painting: querying art with photos", "University of Oxford", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Enhanced Pictorial Structures for Precise Eye Localization Under Uncontrolled Conditions", "Nanjing University, Nanjing 210093, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Finding iconic images", "Computer Science Dept., SUNY Stony Brook, USA", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["A real-time and unsupervised face Re-Identification system for Human-Robot Interaction", "Intelligent Behaviour Understanding Group, Imperial College London, London, UK", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Face Alignment Using a Ranking Model based on Regression Trees", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["Scalable face image retrieval integrating multi-feature quantization and constrained reference re-ranking", "State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems", "", "University of Basel", "University of Basel", 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Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Face recognition based on dictionary learning with the locality constraints of atoms", "Guangdong Industry Training Centre, Guangdong Polytechnic Normal University, Guangzhou, China", "Guangdong Polytechnic Normal University", "Guangdong Industry Training Centre, Guangdong Polytechnic Normal University, Guangzhou, China", "Tianhe Park, Tianhe, Guangzhou, China, 510640", "23.13170700", "113.37164300", "edu", ""], ["Weakly Supervised Learning for Unconstrained Face Processing", "University of Massachusetts - Amherst", "University of Massachusetts", "University of Massachusetts", "University of Massachusetts, Hicks Way, Amherst, Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["Partial face recognition: An alignment free approach", "Michigan State University, East Lansing, MI 48824, U.S.A", 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University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Analyzing impact of image scaling algorithms on viola-jones face detection framework", "IC Design Group, CSIR-Central Electronics Engineering Research Institute, Pilani, Rajasthan, India", "Central Electronics Research Institute, Pilani, India", "Advanced Electronics System, Academy of Scientific and Industrial Research, CSIR-Central Electronics Research Institute, Pilani, India", "Central Electronics Engineering Research Institute, Pilani, Rajasthan 333031, India", "28.36561930", "75.58349530", "edu", ""], ["Avatar digitization from a single image for real-time rendering", "University of Southern California", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", 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"University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Displaced dynamic expression regression for real-time facial tracking and animation", "Zhejiang University", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["Modular hierarchical feature learning with deep neural networks for face verification", "State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", 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University of Sassari, Viale Mancini 5, 07100 Sassari, Italy", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Efficient object detection using cascades of nearest convex model classifiers", "Eskisehir Osmangazi University", "Eskisehir Osmangazi University", "Eskisehir Osmangazi University", "Eski\u015fehir Osmangazi \u00dcniversitesi Me\u015felik Yerle\u015fkesi, K\u00fctahya-Eski\u015fehir yolu, Sazova Mahallesi, Karag\u00f6zler, Tepeba\u015f\u0131, Eski\u015fehir, \u0130\u00e7 Anadolu B\u00f6lgesi, 26160, T\u00fcrkiye", "39.74875160", "30.47653071", "edu", ""], ["A Multi-level Contextual Model for Person Recognition in Photo Albums", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology, River Terrace, Hoboken, Hudson County, New Jersey, 07030, USA", "40.74225200", "-74.02709490", "edu", ""], ["The Devil of Face Recognition is in the Noise", "University of California San Diego", "University of California, San Diego", "University of California, San Diego", "UCSD, 9500, Gilman Drive, Sixth College, University City, San Diego, San Diego County, California, 92093, USA", "32.87935255", "-117.23110049", "edu", ""], ["Watchlist risk assessment using multiparametric cost and relative entropy", "Biometric Technologies Laboratory, Department of Electrical and Computer Engineering, University of Calgary, Alberta, T2N 1N4 Canada", "University of Calgary", "University of Calgary", "University of Calgary, Service Tunnel, University Heights, Calgary, Alberta, T2N 1N7, Canada", "51.07840380", "-114.12870770", "edu", ""], ["Dynamic Local Ternary Pattern for Face Recognition and Verification", "University of Dhaka, Bangladesh", "University of Dhaka", "University of Dhaka", "World War Memorial, Shahid Minar Rd, Jagannath Hall, DU, 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University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Learning Kernel Extended Dictionary for Face Recognition", "Department of Mathematics, Intelligent Data Center, Sun Yat-sen University, Guangzhou, China", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["Predicting Image Memorability Through Adaptive Transfer Learning From External Sources", "School of Electronics and Information Engineering, Tianjin University, Tianjin, China", "Tianjin University", "Tianjin University", "\u6cf0\u5c71\u822a\u7a7a\u6e2f/\u5929\u6d25\u5927\u53a6, \u67a3\u884c\u8def, \u67a3\u884c \u9ad8\u738b\u5bfa, \u957f\u57ce\u8def, \u5927\u6cb3, \u5cb1\u5cb3\u533a (Daiyue), \u6cf0\u5b89\u5e02, \u5c71\u4e1c\u7701, 271000, \u4e2d\u56fd", "36.20304395", "117.05842113", "edu", ""], ["Learning invariant representations and applications to face verification", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research (MIT), Main Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.36262950", "-71.09144810", "edu", ""], ["Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector", "Faculty of Electrical Engineering, Czech Technical University in Prague", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed 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Francisco Tomas y Valiente, 11 - Campus de Cantoblanco - 28049 Madrid, Spain", "Universidad Autonoma de Madrid", "Universidad Autonoma de Madrid", "Facultad de Medicina de la Universidad Aut\u00f3noma de Madrid, Calle de Arturo Duperier, Fuencarral, Fuencarral-El Pardo, Madrid, \u00c1rea metropolitana de Madrid y Corredor del Henares, Comunidad de Madrid, 28001, Espa\u00f1a", "40.48256135", "-3.69060790", "edu", ""], ["Adversarial Attack Type I: Generating False Positives", "", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification", "University of Maryland, College Park", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Grouper: Optimizing Crowdsourced Face Annotations", "Michigan State University", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization", "Faculty of Electrical Engineering, University of Ljubljana, Tr za ska cesta", "University of Ljubljana", "University of Ljubljana", "UL Fakulteta za ra\u010dunalni\u0161tvo in informatiko, 113, Ve\u010dna pot, Vrta\u010da, Ro\u017ena dolina, Ljubljana, Upravna Enota Ljubljana, Osrednjeslovenska, 1000, Slovenija", "46.05015580", "14.46907327", "edu", ""], ["Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition", "University of Groningen, The Netherlands", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["Learning the Face Prior for Bayesian Face Recognition", "The Chinese University of Hong Kong, China", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition", "", "Facebook", "Facebook", "250 Bryant St, Mountain View, CA 94041, USA", "37.39367170", "-122.08072620", "company", "Facebook, Mountain View, CA"], ["Context-Aware Local Binary Feature Learning for Face Recognition", "Department of Automation, State Key Lab of Intelligent Technologies and Systems and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Investigating Nuisances in DCNN-Based Face Recognition", "Department of Information Engineering, University of Florence, Florence, Italy", "University of Florence", "University of Florence", "Piazza di San Marco, 4, 50121 Firenze FI, Italy", "43.77764260", "11.25976500", "edu", ""], ["Non-parametric Bayesian Constrained Local Models", "Institute of Systems and Robotics, University of Coimbra, Portugal", "Institute of Systems and Robotics", "Institute of Systems and Robotics", "Institut f\u00fcr Robotik und Kognitive Systeme, 160, Ratzeburger Allee, Strecknitz, Sankt J\u00fcrgen, Strecknitz, L\u00fcbeck, Schleswig-Holstein, 23562, Deutschland", "53.83383710", "10.70359390", "edu", ""], ["Effective 3D based frontalization for unconstrained face recognition", "Media Integration and Communication Center - 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All rights reserved", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection", "Institute of Arti cial Intelligence and Cognitive Engineering (ALICE), University of Groningen", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["Elastic preserving projections based on L1-norm maximization", "School of Electronic and Information Engineering, Beihang University, Beijing, China", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["HyperFace: A Deep Multi-task Learning Framework for 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NY, USA", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Landmark Free Face Attribute Prediction", "School of Computing, National University of Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Multi-objective convolutional learning for face labeling", "UC Merced, USA", "University of California, Merced", "University of California, Merced", "University of California, Merced, Ansel Adams Road, Merced County, California, USA", "37.36566745", "-120.42158888", "edu", ""], ["Multi-Task Convolutional Neural Network for Face Recognition", "Michigan State University, East Lansing MI", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["One-to-many face recognition with bilinear CNNs", "University of Massachusetts, Amherst", "University of Massachusetts", "University of Massachusetts", "University of Massachusetts, Hicks Way, Amherst, Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["A Wearable IoT with Complex Artificial Perception Embedding for Alzheimer Patients", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA", "33.58667840", "-101.87539204", "edu", ""], ["One Shot Similarity Metric Learning for Action Recognition", "The Weizmann Institute of", "Weizmann Institute of Science", "Weizmann Institute of Science", "\u05de\u05db\u05d5\u05df \u05d5\u05d9\u05e6\u05de\u05df 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Gainesville Municipal Boundaries, Alachua County, Florida, 32611, USA", "29.63287840", "-82.34901330", "edu", ""], ["PDSS: patch-descriptor-similarity space for effective face verification", "Peking University, Beijing, China", "Peking University", "Peking University", "\u5317\u4eac\u5927\u5b66, 5\u53f7, \u9890\u548c\u56ed\u8def, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100871, \u4e2d\u56fd", "39.99223790", "116.30393816", "edu", ""], ["Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model", "", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Face Synthesis from Facial Identity Features", "University of Massachusetts Amherst", "University of Massachusetts", "University of Massachusetts", "University of Massachusetts, Hicks Way, Amherst, Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes", "School of Computer Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Deep tree-structured face: A unified representation for multi-task facial biometrics", "University of Tennessee, Knoxville", "University of Tennessee", "University of Tennessee", "University of Tennessee, Melrose Avenue, Fort Sanders, Knoxville, Knox County, Tennessee, 37916, USA", "35.95424930", "-83.93073950", "edu", ""], ["Face recognition based on manifold constrained joint sparse sensing with K-SVD", "Xinjiang Vocational and Technical College of Communications, Wulumuqi, People\u2019s Republic of China", "Shanghai University", "Shanghai University", "\u4e0a\u6d77\u5927\u5b66, \u9526\u79cb\u8def, \u5927\u573a\u9547, \u5b9d\u5c71\u533a (Baoshan), \u4e0a\u6d77\u5e02, 201906, \u4e2d\u56fd", "31.32235655", "121.38400941", "edu", ""], ["Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images", "Department of Computer Science and Technology, Tongji University, Shanghai, China", "Tongji University", "Tongji University", "\u540c\u6d4e\u5927\u5b66, 1239, \u56db\u5e73\u8def, \u6c5f\u6e7e, \u8679\u53e3\u533a, \u4e0a\u6d77\u5e02, 200092, \u4e2d\u56fd", "31.28473925", "121.49694909", "edu", ""], ["A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks", "Faculty of Information Technology, Beijing University of Technology, Beijing, China", "Beijing University of Technology", "Beijing University of Technology", "\u5317\u4eac\u5de5\u4e1a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u5927\u90ca\u4ead\u6751, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 3208, \u4e2d\u56fd", "39.87391435", "116.47722285", "edu", ""], ["Data-specific Adaptive Threshold for Face Recognition and Authentication", "", "Institute of Information Science", "Institute of Information Science", "\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240, \u6578\u7406\u5927\u9053, \u4e2d\u7814\u91cc, \u5357\u6e2f\u5b50, \u5357\u6e2f\u5340, \u81fa\u5317\u5e02, 11574, \u81fa\u7063", "25.04107280", "121.61475620", "edu", ""], ["A Scalable and Privacy-Aware IoT Service for Live Video Analytics", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Deeply learned face representations are sparse, selective, and robust", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Dual Linear Regression Based Classification for Face Cluster Recognition", "University of Northern British Columbia", "University of Northern British Columbia", "University of Northern British Columbia", "UNBC, Campus Ring Road, College Heights, Prince George, Regional District of Fraser-Fort George, British Columbia, V2M 5K7, Canada", "53.89256620", "-122.81471592", "edu", ""], ["Face , Age and Gender Recognition using Local Descriptors", "University of Ottawa", "University of Ottawa", "University of Ottawa", "University of Ottawa, 1, Stewart Street, Byward Market, Lowertown, Rideau-Vanier, Ottawa, Ontario, K1N 6N5, Canada", "45.42580475", "-75.68740118", "edu", ""], ["Annotated crowd video face database", "", "Indian Institute of Technology Delhi", "IIIT-Delhi, India", "IIIT-Delhi, Mathura Road, Friends 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\u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Mean-Variance Loss for Deep Age Estimation from a Face", "University of Chinese Academy of Sciences, Beijing, 100049, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Rank Minimization across Appearance and Shape for AAM Ensemble Fitting", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology, Macgregor Lane, Merthyr, South Brisbane, Brisbane, QLD, 4000, Australia", "-27.47715625", "153.02841004", "edu", ""], ["Unsupervised Training for 3D Morphable Model Regression", "", "MIT CSAIL", "MIT CSAIL", "32 Vassar St, Cambridge, MA 02139, USA", "42.36194070", "-71.09043780", "edu", ""], ["A Novel Two-stage Learning Pipeline for Deep Neural Networks", "University of Science and Technology of China, Hefei, China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Toward Open-Set Face Recognition", "Vision and Security Technology Lab, University of Colorado Colorado Springs", "University of Colorado, Colorado Springs", "University of Colorado Colorado Springs", "Main Hall, The Spine, Colorado Springs, El Paso County, Colorado, 80907, USA", "38.89207560", "-104.79716389", "edu", ""], ["Face recognition in the wild with the Probabilistic Gabor-Fisher 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University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["A Community Detection Approach to Cleaning Extremely Large Face Database", "National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China", "National University of Defense Technology, China", "National University of Defence Technology, Changsha 410000, China", "\u56fd\u9632\u79d1\u5b66\u6280\u672f\u5927\u5b66, \u4e09\u4e00\u5927\u9053, \u5f00\u798f\u533a, \u5f00\u798f\u533a (Kaifu), \u957f\u6c99\u5e02 / Changsha, \u6e56\u5357\u7701, 410073, \u4e2d\u56fd", "28.22902090", "112.99483204", "edu", ""], ["Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition", "West Virginia University", "West Virginia University", "West Virginia University", "88, Windsor Avenue, The Flatts, Morgantown, Monongalia County, West Virginia, 26505, USA", "39.65404635", "-79.96475355", "edu", ""], ["Exploring computation-communication tradeoffs in camera systems", "University of Washington", "University of Washington", "University of Washington", "University of Washington, Rainier Vista, Montlake, University District, Seattle, King County, Washington, 98195, USA", "47.65432380", "-122.30800894", "edu", ""], ["Making a Science of Model Search", "Rowland Institute at Harvard", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["Simultaneous Cascaded Regression", "", "Institute of Systems and Robotics", "Institute of Systems and Robotics", "Institut f\u00fcr Robotik und Kognitive Systeme, 160, Ratzeburger Allee, Strecknitz, Sankt J\u00fcrgen, Strecknitz, L\u00fcbeck, Schleswig-Holstein, 23562, Deutschland", "53.83383710", "10.70359390", "edu", ""], ["Learning Discriminative Features with Class Encoder", "University of Chinese Academy of Science", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features", "Department of Electronic EngineeringCentre for Vision, Speech and Signal Processing, University of Surrey, Surrey, U.K.", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation", "University of Science and Technology of China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Training Deep Face Recognition Systems with Synthetic Data", "University of Basel", "University of Basel", "University of Basel", "Faculty of Psychology, University of Basel, 60-62, Missionsstrasse, Grossbasel, Am Ring, Basel, Basel-Stadt, 4055, Schweiz/Suisse/Svizzera/Svizra", "47.56126510", "7.57529610", "edu", ""], ["Joint Face Alignment and 3D Face Reconstruction", "Michigan State University, East Lansing, MI, U.S.A", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition", "Shenzhen Key Lab of Comp. Vis. and Pat. Rec., Shenzhen Institutes of Advanced Technology, CAS, China", "Shenzhen Institutes of Advanced Technology", "Shenzhen Institutes of Advanced Technology", "\u4e2d\u56fd\u79d1\u5b66\u9662\u6df1\u5733\u5148\u8fdb\u6280\u672f\u7814\u7a76\u9662, 1068, \u79d1\u7814\u8def, \u6df1\u5733\u5927\u5b66\u57ce, \u4e09\u5751\u6751, \u5357\u5c71\u533a, \u6df1\u5733\u5e02, \u5e7f\u4e1c\u7701, 518000, \u4e2d\u56fd", "22.59805605", "113.98533784", "edu", ""], ["Weakly Supervised Facial Analysis with Dense Hyper-Column Features", "Carnegie Mellon University, Pittsburgh, PA, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Partial face detection for continuous authentication", "UMIACS, University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["An Efficient Joint Formulation for Bayesian Face Verification", "Visual Computing Group, Microsoft Research, Beijing, China", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Complexity of multiverse networks and their multilayer generalization", "Tel Aviv University", "Tel Aviv University", "Tel Aviv University", "\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05ea \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, \u05db\u05d9\u05db\u05e8 \u05de\u05e0\u05d3\u05dc, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1 - \u05d9\u05e4\u05d5, \u05d0\u05e4\u05e7\u05d4, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1-\u05d9\u05e4\u05d5, \u05de\u05d7\u05d5\u05d6 \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.11198890", "34.80459702", "edu", ""], ["Randomized Intraclass-Distance Minimizing Binary Codes for face 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De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets", "University of Wisconsin   Madison", "University of Wisconsin Madison", "University of Wisconsin Madison", "University of Wisconsin-Madison, Marsh Lane, Madison, Dane County, Wisconsin, 53705-2221, USA", "43.07982815", "-89.43066425", "edu", ""], ["Generating a Large, Freely-Available Dataset for Face-Related Algorithms", "Amherst College", "Amherst College", "Amherst College", "Amherst College, Boltwood Avenue, Amherst, Hampshire, Massachusetts, 01004, USA", "42.37289000", "-72.51881400", "edu", ""], ["Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning", "Department of Electrical and Computer Engineering, College of the Computer and Information Science, Northeastern University, Boston, MA, USA", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Markov Chain Monte Carlo for Automated Face Image Analysis", "Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland", "University of Basel", "University of Basel", "Faculty of Psychology, University of Basel, 60-62, Missionsstrasse, Grossbasel, Am Ring, Basel, Basel-Stadt, 4055, Schweiz/Suisse/Svizzera/Svizra", "47.56126510", "7.57529610", "edu", ""], ["Local Large-Margin Multi-Metric Learning for Face and Kinship Verification", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis", "University of Chinese Academy of Sciences, Beijing, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Exploring the geo-dependence of human face appearance", "University of Kentucky", "University of Kentucky", "University of Kentucky", "University of Kentucky, Columbia Avenue, Sorority Circle, Lexington, Fayette County, Kentucky, 40508, USA", "38.03337420", "-84.50177580", "edu", ""], ["On the Applications of Robust PCA in Image and Video Processing", "Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Sparsely Encoded Local Descriptor for face recognition", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Chinese Academy of Science", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Beijing, China", "39.90419990", "116.40739630", "edu", ""], ["Inference Attacks Against Collaborative Learning", "UCL and Alan Turing Institute", "Cornell University", "Cornell University", "Cornell University, Forest Home Drive, Forest Home, Tompkins County, New York, 14853, USA", "42.45055070", "-76.47835130", "edu", ""], ["Deep Transfer Metric Learning", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. 2Advanced Digital Sciences Center, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Towards Pose-robust Face Recognition on Video", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology, Macgregor Lane, Merthyr, South Brisbane, Brisbane, QLD, 4000, Australia", "-27.47715625", "153.02841004", "edu", ""], ["Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling", "University of California at Berkeley / ICSI", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Learning Symmetry Features for Face Detection Based on Sparse Group Lasso", "National Laboratory of Pattern Recognition, Institute of Automation", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Constructing Local Binary Pattern Statistics by Soft Voting", "", "University of Oulu", "University of Oulu", "Oulun yliopisto, Biologintie, Linnanmaa, Oulu, Oulun seutukunta, Pohjois-Pohjanmaa, Pohjois-Suomen aluehallintovirasto, Pohjois-Suomi, Manner-Suomi, 90540, Suomi", "65.05921570", "25.46632601", "edu", ""], ["Learning Structured Output Representation using Deep Conditional Generative Models", "University of Michigan, Ann Arbor", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Online, Real-Time Tracking Using a Category-to-Individual Detector", "California Institute of Technology, USA", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["High-Order Local Spatial Context Modeling by Spatialized Random Forest", "Hefei University of Technology, Hefei, China", "Hefei University of Technology", "Hefei University of Technology", "\u5408\u80a5\u5de5\u4e1a\u5927\u5b66\uff08\u5c6f\u6eaa\u8def\u6821\u533a\uff09, 193\u53f7, \u5357\u4e00\u73af\u8def, \u822a\u8fd0\u5357\u6751, \u5305\u516c\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230009, \u4e2d\u56fd", "31.84691800", "117.29053367", "edu", ""], ["Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures", "Rowland Institute at Harvard", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Learning Face Image Quality From Human Assessments", "Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["VIPLFaceNet: an open source deep face recognition SDK", "University of Chinese Academy of Sciences, Beijing 100049, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["On-line machine learning accelerator on digital RRAM-crossbar", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Hierarchical-PEP model for real-world face recognition", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology, River Terrace, Hoboken, Hudson County, New Jersey, 07030, USA", "40.74225200", "-74.02709490", "edu", ""], ["Displacement Template with Divide-&-Conquer Algorithm for Significantly Improving Descriptor Based Face Recognition Approaches", "Wenzhou University, China", "University of Northern British Columbia", "University of Northern British Columbia", "UNBC, Campus Ring Road, College Heights, Prince George, Regional District of Fraser-Fort George, British Columbia, V2M 5K7, Canada", "53.89256620", "-122.81471592", "edu", ""], ["Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization", "The Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "hong kong, 11, \u80b2\u624d\u9053 Yuk Choi Road, \u5c16\u6c99\u5480 Tsim Sha Tsui, \u6cb9\u5c16\u65fa\u5340 Yau Tsim Mong District, \u4e5d\u9f8d Kowloon, HK, 00000, \u4e2d\u56fd", "22.30457200", "114.17976285", "edu", ""], ["Selective Encoding for Recognizing Unreliably Localized Faces", "University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Report on the FG 2015 Video Person Recognition Evaluation", "University of Technology, Sydney", "University of Technology Sydney", "University of Technology Sydney", "University of Technology Sydney, Omnibus Lane, Ultimo, Sydney, NSW, 2007, Australia", "-33.88096510", "151.20107299", "edu", ""], ["Face Verification with Multi-Task and Multi-Scale Features Fusion", "", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Variational Relevance Vector Machine for Tabular Data", "The Blavatnik School of Computer Science, The Tel-Aviv University", "Tel Aviv University", "Tel Aviv University", "\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05ea \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, \u05db\u05d9\u05db\u05e8 \u05de\u05e0\u05d3\u05dc, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1 - \u05d9\u05e4\u05d5, \u05d0\u05e4\u05e7\u05d4, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1-\u05d9\u05e4\u05d5, \u05de\u05d7\u05d5\u05d6 \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.11198890", "34.80459702", "edu", ""], ["Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition", "School of Engineering, Griffith University, Nathan, QLD, Australia", "Griffith University", "Griffith University", "Griffith University Nathan Campus, Johnson Path, Nathan, Nathan Heights, QLD, 4111, Australia", "-27.55339750", "153.05336234", "edu", ""], ["Learning Structure and Strength of CNN Filters for Small Sample Size Training", "", "Indian Institute of Technology Delhi", "IIIT-Delhi, India", "IIIT-Delhi, Mathura Road, Friends Colony, South East Delhi, Delhi, 110020, India", "28.54632595", "77.27325504", "edu", ""], ["Robust Point Set Matching for Partial Face Recognition", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Learning structured sparse representation for single sample face recognition", "School of Electrical Engineering, Nantong University, Nantong, China", "Nantong University", "Nantong University", "\u5357\u901a\u5927\u5b66, \u72fc\u5c71\u9547\u8857\u9053, \u5d07\u5ddd\u533a (Chongchuan), \u5357\u901a\u5e02 / Nantong, \u6c5f\u82cf\u7701, 226000, \u4e2d\u56fd", "31.97474630", "120.90779264", "edu", ""], ["Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning", "College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China", "Zhejiang University of Technology", "Zhejiang University of Technology", "\u6d59\u6c5f\u5de5\u4e1a\u5927\u5b66, \u6f6e\u738b\u8def, \u671d\u6656\u8857\u9053, \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310014, \u4e2d\u56fd", "30.29315340", "120.16204580", "edu", ""], ["Unconstrained face recognition: Establishing baseline human performance via crowdsourcing", "Michigan State University, East Lansing, MI, U.S.A", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["AgeDB: The First Manually Collected, In-the-Wild Age Database", "Middlesex University London", "Middlesex University", "Middlesex University", "Middlesex University, Greyhound Hill, Hendon, The Hyde, London Borough of Barnet, London, Greater London, England, NW4 4JP, UK", "51.59029705", "-0.22963221", "edu", ""], ["Applying scattering operators for face recognition: A comparative study", "", "National Taiwan University", "National Taiwan University", "\u81fa\u5927;\u53f0\u5927, 1, \u7f85\u65af\u798f\u8def\u56db\u6bb5, \u5b78\u5e9c\u91cc, \u5927\u5b89\u5340, \u81fa\u5317\u5e02, 10617, \u81fa\u7063", "25.01682835", "121.53846924", "edu", ""], ["CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval", "University of Caen, France", "University of Caen", "University of Caen", "\u4eac\u90fd\u5927\u5b66, \u4eca\u51fa\u5ddd\u901a, \u5409\u7530\u6cc9\u6bbf\u753a, \u5de6\u4eac\u533a, \u4eac\u90fd\u5e02, \u4eac\u90fd\u5e9c, \u8fd1\u757f\u5730\u65b9, 606-8501, \u65e5\u672c", "35.02749960", "135.78154513", "edu", ""], ["Faceless Person Recognition; Privacy Implications in Social Media", "Max-Planck Institute for Informatics", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["Deep Multi-Center Learning for Face Alignment", "Shanghai Jiao Tong University, China", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Discriminative Elastic-Net Regularized Linear Regression", "School of Computing Sciences, University of East Anglia, Norwich, U.K.", "University of East Anglia", "University of East Anglia", "Arts (Lower Walkway Level), The Square, Westfield View, Earlham, Norwich, Norfolk, East of England, England, NR4 7TJ, UK", "52.62215710", "1.24091360", "edu", ""], ["Deep Convolutional Network Cascade for Facial Point Detection", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Teaching Computer Vision: Bringing Research Benchmarks to the Classroom", "The Open University of Israel, Israel", "Open University of Israel", "Open University of Israel", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 \u05d4\u05e4\u05ea\u05d5\u05d7\u05d4, 15, \u05d0\u05d1\u05d0 \u05d7\u05d5\u05e9\u05d9, \u05d7\u05d9\u05e4\u05d4, \u05d2\u05d1\u05e2\u05ea \u05d3\u05d0\u05d5\u05e0\u05e1, \u05d7\u05d9\u05e4\u05d4, \u05de\u05d7\u05d5\u05d6 \u05d7\u05d9\u05e4\u05d4, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.77824165", "34.99565673", "edu", ""], ["Kinship Verification through Transfer Learning", "and Southeast University, China", "SUNY Buffalo", "SUNY Buffalo", "SUNY College at Buffalo, Academic Drive, Elmwood Village, Buffalo, Erie County, New York, 14222, USA", "42.93362780", "-78.88394479", "edu", ""], ["Quality Aware Network for Set to Set Recognition", "University of Sydney", "University of Sydney", "University of Sydney", "USyd, Fisher Road, Camperdown, Sydney, NSW, 2006, Australia", "-33.88890695", "151.18943366", "edu", ""], ["Is block matching an alternative tool to LBP for face recognition?", "KTH Royal Institute of Technology, Stockholm, Sweden", "KTH Royal Institute of Technology, Stockholm", "KTH   Royal Institute of Technology, Stockholm", "KTH, Teknikringen, L\u00e4rkstaden, Norra Djurg\u00e5rden, \u00d6stermalms stadsdelsomr\u00e5de, Sthlm, Stockholm, Stockholms l\u00e4n, Svealand, 114 28, Sverige", "59.34986645", "18.07063213", "edu", ""], ["4D unconstrained real-time face recognition using a commodity depth camera", "University of Groningen, Nijenborgh 9, 9747 AG, The Netherlands", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["V-shaped interval insensitive loss for ordinal classification", "Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czech Republic", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed m\u011bsto Praha, Hlavn\u00ed m\u011bsto Praha, Praha, 11121, \u010cesko", "50.07642960", "14.41802312", "edu", ""], ["Learning One-Shot Exemplar SVM from the Web for Face Verification", "", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Boosting Pseudo Census Transform Features for Face Alignment", "", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["FaceTracer: A Search Engine for Large Collections of Images with Faces", "Columbia University", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples", "", "Purdue University", "Purdue University", "Purdue University, West Stadium Avenue, West Lafayette, Tippecanoe County, Indiana, 47907, USA", "40.43197220", "-86.92389368", "edu", ""], ["A talking profile to distinguish identical twins", "School of Computing, National University of Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Active Rare Class Discovery and Classification Using Dirichlet Processes", "School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", ""], ["A Critical Review of Action Recognition Benchmarks", "The Open University of Israel", "Open University of Israel", "Open University of Israel", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 \u05d4\u05e4\u05ea\u05d5\u05d7\u05d4, 15, \u05d0\u05d1\u05d0 \u05d7\u05d5\u05e9\u05d9, \u05d7\u05d9\u05e4\u05d4, \u05d2\u05d1\u05e2\u05ea \u05d3\u05d0\u05d5\u05e0\u05e1, \u05d7\u05d9\u05e4\u05d4, \u05de\u05d7\u05d5\u05d6 \u05d7\u05d9\u05e4\u05d4, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.77824165", "34.99565673", "edu", ""], ["Utility data annotation with Amazon Mechanical Turk", "University of Illinois at Urbana-Champaign, 201 N Goodwin, 61820, USA", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Learning Compositional Visual Concepts with Mutual Consistency", "School of Electrical and Computer Engineering, Cornell University, Ithaca NY", "Cornell University", "Cornell University", "Cornell University, Forest Home Drive, Forest Home, Tompkins County, New York, 14853, USA", "42.45055070", "-76.47835130", "edu", ""], ["Face Verification Using Boosted Cross-Image Features", "University of Central Florida", "University of Central Florida", "University of Central Florida", "University of Central Florida, Libra Drive, University Park, Orange County, Florida, 32816, USA", "28.59899755", "-81.19712501", "edu", ""], ["Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers", "University of Wisconsin   Madison, USA", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Adventures in archiving and using three years of webcam images", "Washington University, St. Louis, MO, USA", "Washington University", "Washington University", "Dero, Wallace Drive, St. Louis County, Missouri, MO 63130, USA", "38.64804450", "-90.30996670", "edu", ""], ["Transform-invariant dictionary learning for face recognition", "Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / 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discriminant analysis for face recognition", "Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece", "Aristotle University of Thessaloniki", "Aristotle University of Thessaloniki", "\u0391\u03c1\u03b9\u03c3\u03c4\u03bf\u03c4\u03ad\u03bb\u03b5\u03b9\u03bf \u03a0\u03b1\u03bd\u03b5\u03c0\u03b9\u03c3\u03c4\u03ae\u03bc\u03b9\u03bf \u0398\u03b5\u03c3\u03c3\u03b1\u03bb\u03bf\u03bd\u03af\u03ba\u03b7\u03c2, \u0395\u03b3\u03bd\u03b1\u03c4\u03af\u03b1, \u03a3\u03b1\u03c1\u03ac\u03bd\u03c4\u03b1 \u0395\u03ba\u03ba\u03bb\u03b7\u03c3\u03af\u03b5\u03c2, \u0395\u03c5\u03b1\u03b3\u03b3\u03b5\u03bb\u03af\u03c3\u03c4\u03c1\u03b9\u03b1, \u0398\u03b5\u03c3\u03c3\u03b1\u03bb\u03bf\u03bd\u03af\u03ba\u03b7, \u0394\u03ae\u03bc\u03bf\u03c2 \u0398\u03b5\u03c3\u03c3\u03b1\u03bb\u03bf\u03bd\u03af\u03ba\u03b7\u03c2, \u03a0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03ae \u0395\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 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Sign Language (ASL)", "Rutgers University, Computer and Information Sciences, 110 Frelinghuysen Road, Piscataway, NJ", "Rutgers University", "Rutgers University", "Rutgers Cook Campus - North, Biel Road, New Brunswick, Middlesex County, New Jersey, 08901, USA", "40.47913175", "-74.43168868", "edu", ""], ["Deep nonlinear metric learning with independent subspace analysis for face verification", "Institute of Automation, Chinese Academy of Sciences, Beijing, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Face recognition based on convolution siamese networks", "The Institute of Optics and Electronics Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Chengdu, China", "University of the Chinese Academy of Sciences", "The Institute of Optics and Electronics Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Chengdu, China", "Chengdu, Sichuan, China", "30.57281500", "104.06680100", "edu", ""], ["Automatic Facial Image Annotation and Retrieval by Integrating Voice Label and Visual Appearance", "National Taiwan University, Taipei, Taiwan Roc", "National Taiwan University", "National Taiwan University", "\u81fa\u5927;\u53f0\u5927, 1, \u7f85\u65af\u798f\u8def\u56db\u6bb5, \u5b78\u5e9c\u91cc, \u5927\u5b89\u5340, \u81fa\u5317\u5e02, 10617, \u81fa\u7063", "25.01682835", "121.53846924", "edu", ""], ["How Related Exemplars Help Complex Event Detection in Web Videos?", "School of Computer Science, Carnegie Mellon University, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Weighted sparse representation using a learned distance metric for face recognition", "Department of Information Management and Security, Korea University", "Korea University", "Korea University", "\uace0\ub824\ub300, \uc548\uc554\ub85c, \uc81c\uae30\ub3d9, \ub3d9\ub300\ubb38\uad6c, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, 02796, \ub300\ud55c\ubbfc\uad6d", "37.59014110", "127.03623180", "edu", ""], ["Personalized Age Progression with Aging Dictionary", "School of Computer Science and Engineering, Nanjing University of Science and Technology", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Person Identity Label Propagation in Stereo Videos", "Aristotle University of Thessaloniki", "Aristotle University of Thessaloniki", "Aristotle University of Thessaloniki", "\u0391\u03c1\u03b9\u03c3\u03c4\u03bf\u03c4\u03ad\u03bb\u03b5\u03b9\u03bf 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54124, \u0395\u03bb\u03bb\u03ac\u03b4\u03b1", "40.62984145", "22.95889350", "edu", ""], ["House in the (Biometric) Cloud: A Possible Application", "Sapienza Univertsity of Rome", "Sapienza Univertsity of Rome", "Sapienza Univertsity of Rome", "Piazzale Aldo Moro, 5, 00185 Roma RM, Italy", "41.90376260", "12.51443840", "edu", ""], ["Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching", "Tafresh University, Tafresh, Iran", "Tafresh University", "Tafresh University", "\u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u062a\u0641\u0631\u0634, \u067e\u0627\u0633\u062f\u0627\u0631\u0627\u0646, \u062e\u0631\u0627\u0632\u0627\u0646, \u0628\u062e\u0634 \u0645\u0631\u06a9\u0632\u06cc, \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u062a\u0641\u0631\u0634, \u0627\u0633\u062a\u0627\u0646 \u0645\u0631\u06a9\u0632\u06cc, \u200f\u0627\u06cc\u0631\u0627\u0646\u200e", "34.68092465", "50.05341352", "edu", ""], ["Hierarchical face parsing via deep learning", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["A feature subtraction method for image based kinship verification under uncontrolled environments", "Department of Electronic Systems, Aalborg University, Denmark", "Aalborg University", "Aalborg University", "AAU, Pontoppidanstr\u00e6de, S\u00f8nder Tranders, Aalborg, Aalborg Kommune, Region Nordjylland, 9220, Danmark", "57.01590275", "9.97532827", "edu", ""], ["Rectified Linear Units Improve Restricted Boltzmann Machines", "University of Toronto, Toronto, ON M5S 2G4, Canada", "University of Toronto", "University of Toronto", "University of Toronto, St. George Street, Bloor Street Culture Corridor, Old Toronto, Toronto, Ontario, M5S 1A5, Canada", "43.66333345", "-79.39769975", "edu", ""], ["Face Synthesis for Eyeglass-Robust Face Recognition", "University of Chinese Academy of Sciences, Beijing, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Cosine Similarity Metric Learning for Face Verification", "School of Computer Science, University of Nottingham", "University of Nottingham", "University of Nottingham", "University of Nottingham, Lenton Abbey, Wollaton, City of Nottingham, East Midlands, England, UK", "52.93874280", "-1.20029569", "edu", ""], ["Discrimination-aware Channel Pruning for Deep Neural Networks", "", "University of Texas at Arlington", "University of Texas at Arlington", "University of Texas at Arlington, South Nedderman Drive, Arlington, Tarrant County, Texas, 76010, USA", "32.72836830", "-97.11201835", "edu", ""], ["How interesting images are: An atypicality approach for social networks", "Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, HI, 96822", "University of Hawaii", "University of Hawaii", "University of Hawaii at Manoa, Bachman Place, Lower M\u0101noa, Moiliili, Honolulu, Honolulu County, Hawaii, 96848, USA", "21.29827950", "-157.81869230", "edu", ""], ["How Important Is Weight Symmetry in Backpropagation?", "Center for Brains, Minds and Machines, McGovern Institute, MIT", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research (MIT), Main Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.36262950", "-71.09144810", "edu", ""], ["LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation", "Georgia Institute of Technology", "Georgia Institute of Technology", "Georgia Institute of Technology", "Georgia Tech, Atlantic Drive Northwest, Bellwood, Rockdale, Atlanta, Fulton County, Georgia, 30318, USA", "33.77603300", "-84.39884086", "edu", ""], ["Unconstrained human identification using comparative facial soft biometrics", "University of Southampton", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", ""], ["A Robust and Efficient Doubly Regularized Metric Learning Approach", "CISE, University of Florida, Gainesville, FL", "University of Florida", "University of Florida", "University of Florida, Southwest 16th Avenue, Diamond Village Apartments, City of Gainesville Municipal Boundaries, Alachua County, Florida, 32611, USA", "29.63287840", "-82.34901330", "edu", ""], ["Gender Classification by LUT Based Boosting of Overlapping Block Patterns", "Tampere University of Technology, Tampere, Finland", "Tampere University of Technology", "Tampere University of Technology", "TTY, 10, Korkeakoulunkatu, Finninm\u00e4ki, Hervanta, Tampere, Tampereen seutukunta, Pirkanmaa, L\u00e4nsi- ja Sis\u00e4-Suomen aluehallintovirasto, L\u00e4nsi-Suomi, Manner-Suomi, 33720, Suomi", "61.44964205", "23.85877462", "edu", ""], ["Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification", "North China University of Technology, Beijing 100144 CHINA", "Beijing Institute of Technology", "Beijing Institute of Technology University", "\u5317\u4eac\u7406\u5de5\u5927\u5b66, 5, \u4e2d\u5173\u6751\u5357\u5927\u8857, \u4e2d\u5173\u6751, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100872, \u4e2d\u56fd", "39.95866520", "116.30971281", "edu", ""], ["Rendering or normalization? An analysis of the 3D-aided pose-invariant face recognition", "University of Houston, Houston, TX, USA", "University of Houston", "University of Houston", "UH, 4800, Calhoun Road, Houston, Harris County, Texas, 77004, USA", "29.72079020", "-95.34406271", "edu", ""], ["Computer Vision for Head Pose Estimation: Review of a Competition", "University of Paderborn, Germany", "Tampere University of Technology", "Tampere University of Technology", "TTY, 10, Korkeakoulunkatu, Finninm\u00e4ki, Hervanta, Tampere, Tampereen seutukunta, Pirkanmaa, L\u00e4nsi- ja Sis\u00e4-Suomen aluehallintovirasto, L\u00e4nsi-Suomi, Manner-Suomi, 33720, Suomi", "61.44964205", "23.85877462", "edu", ""], ["The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", "University of Washington", "University of Washington", "University of Washington", "University of Washington, Rainier Vista, Montlake, University District, Seattle, King County, Washington, 98195, USA", "47.65432380", "-122.30800894", "edu", ""], ["Deep or Shallow Facial Descriptors? A Case for Facial Attribute Classification and Face Retrieval", "Multimedia University, Cyberjaya, Malaysia", "Multimedia University", "Multimedia University", "Universiti Multimedia, Persiaran Neuron, Bandar Nusaputra, Cyberjaya, Selangor, 63000, Malaysia", "2.92749755", "101.64185301", "edu", ""], ["Storages Are Not Forever", "School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Learning Rotation-Invariant Local Binary Descriptor", "Department of Automation, State Key Lab of Intelligent Technologies and Systems, and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Bridging the Gap Between Forensics and Biometric-Enabled Watchlists for e-Borders", "Dept of Electrical and Computer Engineering, University of Calgary, Calgary, CANADA", "University of Calgary", "University of Calgary", "University of Calgary, Service Tunnel, University Heights, Calgary, Alberta, T2N 1N7, Canada", "51.07840380", "-114.12870770", "edu", ""], ["Cascaded Shape Space Pruning for Robust Facial Landmark Detection", "Institute of Computing Technology, CAS, Beijing 100190, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["A Deep Sum-Product Architecture for Robust Facial Attributes Analysis", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Partially Shared MultiTask Convolutional Neural Network with Local Constraint for Face Attribute Learning", "Zhejiang University, China", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["Detecting person presence in TV shows with linguistic and structural features", "France Telecom - Orange Labs, Lannion, France", "Orange Labs, Lannion, France", "France Telecom - Orange Labs, Lannion, France", "2 Avenue Pierre Marzin, 22300 Lannion, France", "48.75416800", "-3.45845860", "company", ""], ["BUAA-iCC at ImageCLEF 2015 Scalable Concept Image Annotation Challenge", "School of Information Technology and Management, University of International", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework", "", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Face Painting: querying art with photos", "University of Oxford", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Enhanced Pictorial Structures for Precise Eye Localization Under Uncontrolled Conditions", "Nanjing University, Nanjing 210093, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Finding iconic images", "Computer Science Dept., SUNY Stony Brook, USA", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["A real-time and unsupervised face Re-Identification system for Human-Robot Interaction", "Intelligent Behaviour Understanding Group, Imperial College London, London, UK", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", "edu", ""], ["Face Alignment Using a Ranking Model based on Regression Trees", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["Scalable face image retrieval integrating multi-feature quantization and constrained reference re-ranking", "State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems", "", "University of Basel", "University of Basel", 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"\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Coarse-to-fine facial landmarks localization based on convolutional feature", "State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China", "Beijing Jiaotong University", "Beijing Jiaotong University", "\u5317\u4eac\u4ea4\u901a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100044, \u4e2d\u56fd", "39.94976005", "116.33629046", "edu", ""], ["Simulation of face recognition at a distance by scaling down images", "Dept. of Computer Science and Computer Engineering, University of Louisville, KY, USA", "University of Louisville", "University of Louisville", "University of Louisville, South Brook Street, Louisville, 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Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Face recognition based on dictionary learning with the locality constraints of atoms", "Guangdong Industry Training Centre, Guangdong Polytechnic Normal University, Guangzhou, China", "Guangdong Polytechnic Normal University", "Guangdong Industry Training Centre, Guangdong Polytechnic Normal University, Guangzhou, China", "Tianhe Park, Tianhe, Guangzhou, China, 510640", "23.13170700", "113.37164300", "edu", ""], ["Weakly Supervised Learning for Unconstrained Face Processing", "University of Massachusetts - Amherst", "University of Massachusetts", "University of Massachusetts", "University of Massachusetts, Hicks Way, Amherst, Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["Partial face recognition: An alignment free approach", "Michigan State University, East Lansing, MI 48824, U.S.A", 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University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Analyzing impact of image scaling algorithms on viola-jones face detection framework", "IC Design Group, CSIR-Central Electronics Engineering Research Institute, Pilani, Rajasthan, India", "Central Electronics Research Institute, Pilani, India", "Advanced Electronics System, Academy of Scientific and Industrial Research, CSIR-Central Electronics Research Institute, Pilani, India", "Central Electronics Engineering Research Institute, Pilani, Rajasthan 333031, India", "28.36561930", "75.58349530", "edu", ""], ["Avatar digitization from a single image for real-time rendering", "University of Southern California", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", 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Guangzhou, China", "South China University of Technology", "South China University of Technology", "\u534e\u5357\u7406\u5de5\u5927\u5b66, \u5927\u5b66\u57ce\u4e2d\u73af\u4e1c\u8def, \u5e7f\u5dde\u5927\u5b66\u57ce, \u65b0\u9020, \u756a\u79ba\u533a (Panyu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510006, \u4e2d\u56fd", "23.05020420", "113.39880323", "edu", ""], ["Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression", "Nanjing, 210094, China, 3 School of Automation, Nanjing University of Posts and Telecommunications", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Unsupervised Discovery of Visual Face Categories", "University of Nevada, Reno, USA", "University of Nevada", "University of Nevada", "Orange 1, Evans Avenue, Reno, Washoe County, Nevada, 89557, USA", "39.54694490", "-119.81346566", "edu", ""], ["Characterizing 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National, Springfield, MO 65804, USA", "University of Bologna", "Universit\u00e0 di Bologna", "Via Zamboni, 33, 40126 Bologna BO, Italy", "44.49623180", "11.35415700", "edu", ""], ["Deep feature selection and projection for cross-age face retrieval", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["A new approach for face detection with omnidirectional sensors", "LITIS, Universite de Rouen - INSA de Rouen, Rouen, FR", "University of Rouen, France", "LITIS, Universite de Rouen - INSA de Rouen, Rouen, FR", "685 Avenue de l'Universit\u00e9, 76800 Saint-\u00c9tienne-du-Rouvray, France", "49.38497570", "1.06832570", "edu", ""], ["State-of-the-art face recognition performance using publicly 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Buffalo, USA and Southeast University, China", "SUNY Buffalo", "SUNY Buffalo", "SUNY College at Buffalo, Academic Drive, Elmwood Village, Buffalo, Erie County, New York, 14222, USA", "42.93362780", "-78.88394479", "edu", ""], ["Implicit Sparse Code Hashing", "Institute of Information Science", "Institute of Information Science", "Institute of Information Science", "\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240, \u6578\u7406\u5927\u9053, \u4e2d\u7814\u91cc, \u5357\u6e2f\u5b50, \u5357\u6e2f\u5340, \u81fa\u5317\u5e02, 11574, \u81fa\u7063", "25.04107280", "121.61475620", "edu", ""], ["Efficient Misalignment-Robust Representation for Real-Time Face Recognition", "The Hong Kong Polytechnic University, Hong Kong", "Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "hong kong, 11, \u80b2\u624d\u9053 Yuk Choi Road, \u5c16\u6c99\u5480 Tsim Sha Tsui, \u6cb9\u5c16\u65fa\u5340 Yau Tsim Mong District, \u4e5d\u9f8d Kowloon, HK, 00000, \u4e2d\u56fd", "22.30457200", "114.17976285", "edu", ""], ["Explicit Shape Regression With Characteristic Number for Facial Landmark Localization", "School of Software, Dalian University of Technology, Dalian, China", "Dalian University of Technology", "Dalian University of Technology", "\u5927\u8fde\u7406\u5de5\u5927\u5b66, \u7ea2\u51cc\u8def, \u7518\u4e95\u5b50\u533a, \u51cc\u6c34\u9547, \u7518\u4e95\u5b50\u533a / Ganjingzi, \u5927\u8fde\u5e02 / Dalian, \u8fbd\u5b81\u7701, 116023, \u4e2d\u56fd", "38.88140235", "121.52281098", "edu", ""], ["Joint Registration and Representation Learning for Unconstrained Face Identification", "University of Canberra, Australia,  Data61 - CSIRO and ANU, Australia", "University of Canberra", "University of Canberra", "University of Canberra, University Drive, Bruce, Belconnen, Australian Capital Territory, 2617, Australia", "-35.23656905", "149.08446994", "edu", ""], ["Face frontalization for Alignment and Recognition", "University of Twente", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Displaced dynamic expression regression for real-time facial tracking and animation", "Zhejiang University", "Zhejiang University", "Zhejiang University", "\u6d59\u6c5f\u5927\u5b66\u4e4b\u6c5f\u6821\u533a, \u4e4b\u6c5f\u8def, \u8f6c\u5858\u8857\u9053, \u897f\u6e56\u533a (Xihu), \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310008, \u4e2d\u56fd", "30.19331415", "120.11930822", "edu", ""], ["Modular hierarchical feature learning with deep neural networks for face verification", "State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models", "Sun Yat-sen University", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis", "Department of Computer Science, Universitat Oberta de Catalunya, Barcelona, Spain", "Universitat Oberta de Catalunya", "Universitat Oberta de Catalunya", "Universitat Oberta de Catalunya, 156, Rambla del Poblenou, Proven\u00e7als del Poblenou, Sant Mart\u00ed, Barcelona, BCN, CAT, 08018, Espa\u00f1a", "41.40657415", "2.19453410", "edu", ""], ["Picture-specific cohort score normalization for face pair matching", "Department of Sciences and Information Technology, University of Sassari, Viale Mancini 5, 07100 Sassari, Italy", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Efficient object detection using cascades of nearest convex model classifiers", "Eskisehir Osmangazi University", "Eskisehir Osmangazi University", "Eskisehir Osmangazi University", "Eski\u015fehir Osmangazi \u00dcniversitesi Me\u015felik Yerle\u015fkesi, K\u00fctahya-Eski\u015fehir yolu, Sazova Mahallesi, Karag\u00f6zler, Tepeba\u015f\u0131, Eski\u015fehir, \u0130\u00e7 Anadolu B\u00f6lgesi, 26160, T\u00fcrkiye", "39.74875160", "30.47653071", "edu", ""], ["A Multi-level Contextual Model for Person Recognition in Photo Albums", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology, River Terrace, Hoboken, Hudson County, New Jersey, 07030, USA", 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"40.47913175", "-74.43168868", "edu", ""], ["Extending explicit shape regression with mixed feature channels and pose priors", "University (ITU", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["Person Recognition in Personal Photo Collections", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction", "School of Software, Tianjin University", "Tianjin University", "Tianjin University", 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"-122.05965487", "edu", ""], ["Supervised Descent Method", "The Robotics Institute", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions", "University of Malta", "University of Malta", "University of Malta", "University of Malta, Ring Road, Japanese Garden, L-Imsida, Malta, MSD 9027, Malta", "35.90232260", "14.48341890", "edu", ""], ["From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data", "Imperial College London, UK", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK", "51.49887085", "-0.17560797", 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"117.17795091", "edu", ""], ["Recent Progress of Face Image Synthesis", "", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Fiducial Facial Point Extraction Using a Novel Projective Invariant", "School of Software, Dalian University of Technology, Dalian, China", "Dalian University of Technology", "Dalian University of Technology", "\u5927\u8fde\u7406\u5de5\u5927\u5b66, \u7ea2\u51cc\u8def, \u7518\u4e95\u5b50\u533a, \u51cc\u6c34\u9547, \u7518\u4e95\u5b50\u533a / Ganjingzi, \u5927\u8fde\u5e02 / Dalian, \u8fbd\u5b81\u7701, 116023, \u4e2d\u56fd", "38.88140235", "121.52281098", "edu", ""], ["Learning Person-Specific Representations From Faces in the Wild", "Institute of Computing, State University of Campinas, Campinas, Brazil", "State University of Campinas", "Institute of Computing, State University of Campinas, Campinas, Brazil", "1353 - Av. Albert Einstein, 1315 - Cidade Universit\u00e1ria, Campinas - SP, 13083-852, Brazil", "-22.81377650", "-47.06400040", "edu", ""], ["Human and sheep facial landmarks localisation by triplet interpolated features", "University of Cambridge", "University of Cambridge", "University of Cambridge", "Clifford Allbutt Lecture Theatre, Robinson Way, Romsey, Cambridge, Cambridgeshire, East of England, England, CB2 0QH, UK", "52.17638955", "0.14308882", "edu", ""], ["Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval", "Indian Institute of Information Technology at Allahabad, Allahabad, India", "IIIT Allahabad, India", "Indian Institute of Information Technology at Allahabad, Allahabad, India", "Indian Institute of Information Technology, Jhalwa, Prayagraj, Uttar Pradesh, India", "25.42991140", "81.77118270", "edu", ""], ["Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild", "University of Bridgeport, Bridgeport, CT 06604, USA", "University of Bridgeport", "University of Bridgeport", "University of Bridgeport, Park Avenue, Bridgeport Downtown South Historic District, Bridgeport, Fairfield County, Connecticut, 06825, USA", "41.16648580", "-73.19205640", "edu", ""], ["Facial feature extraction method based on shallow and deep fusion CNN", "School of Information and Communication, Guilin University of Electronic Technology Guangxi Guilin, China", "Guilin University of Electronic Technology Guangxi Guilin", "Guilin University of Electronic Technology Guangxi Guilin", "\u6842\u6797\u7535\u5b50\u79d1\u6280\u5927\u5b66\u91d1\u9e21\u5cad\u6821\u533a, 1\u53f7, \u91d1\u9e21\u8def, \u4e03\u661f\u533a, \u9ec4\u83ba\u5ca9\u6751, \u4e03\u661f\u533a, \u6842\u6797\u5e02, \u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a, 541004, \u4e2d\u56fd", "25.28739920", "110.33242770", "edu", ""], ["Pre-trained Deep Convolutional Neural Networks for Face Recognition", "facultyofmathematicsandnaturalsciencesarti cialintelligence22-09-2016|1ATitleA.UthorRijksuniversiteitGroningenSomeFaculty", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["On the size of Convolutional Neural Networks and generalization performance", "University of Maryland, College Park", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Surpassing Human-Level Face Verification Performance on LFW with GaussianFace", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Siamese network features for image matching", "Department of Computer Science, Aalto University, Finland", "Aalto University", "Aalto University", "Aalto, 24, Otakaari, Otaniemi, Suur-Tapiola, Espoo, Helsingin seutukunta, Uusimaa, Etel\u00e4-Suomi, Manner-Suomi, 02150, Suomi", "60.18558755", "24.82427330", "edu", ""], ["Hybrid Deep Learning for Face Verification", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-Based Supervision", "Imperial College London", "Imperial College London", "Imperial College London", "Imperial College London, Exhibition Road, Brompton, Royal 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University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Learning Kernel Extended Dictionary for Face Recognition", "Department of Mathematics, Intelligent Data Center, Sun Yat-sen University, Guangzhou, China", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["Predicting Image Memorability Through Adaptive Transfer Learning From External Sources", "School of Electronics and Information Engineering, Tianjin University, Tianjin, China", "Tianjin University", "Tianjin University", "\u6cf0\u5c71\u822a\u7a7a\u6e2f/\u5929\u6d25\u5927\u53a6, \u67a3\u884c\u8def, \u67a3\u884c \u9ad8\u738b\u5bfa, \u957f\u57ce\u8def, \u5927\u6cb3, \u5cb1\u5cb3\u533a (Daiyue), \u6cf0\u5b89\u5e02, \u5c71\u4e1c\u7701, 271000, \u4e2d\u56fd", "36.20304395", "117.05842113", "edu", ""], ["Learning invariant representations and applications to face verification", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research", "McGovern Institute for Brain Research (MIT), Main Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.36262950", "-71.09144810", "edu", ""], ["Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector", "Faculty of Electrical Engineering, Czech Technical University in Prague", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed 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"5.44094480", "10.07120561", "edu", ""], ["A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction", "University of Texas at San Antonio, USA", "University of Texas at San Antonio", "University of Texas at San Antonio", "UTSA, Paseo Principal, San Antonio, Bexar County, Texas, 78249-1620, USA", "29.58333105", "-98.61944505", "edu", ""], ["Facial Recognition with Encoded Local Projections", "", "University of Waterloo", "University of Waterloo", "University of Waterloo, 200, University Avenue West, Northdale, Beechwood, Waterloo, Regional Municipality of Waterloo, Ontario, N2L 3G1, Canada", "43.47061295", "-80.54724732", "edu", ""], ["MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition", "", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Human and algorithm performance on the PaSC face Recognition Challenge", "School of Behavioral and Brain 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Francisco Tomas y Valiente, 11 - Campus de Cantoblanco - 28049 Madrid, Spain", "Universidad Autonoma de Madrid", "Universidad Autonoma de Madrid", "Facultad de Medicina de la Universidad Aut\u00f3noma de Madrid, Calle de Arturo Duperier, Fuencarral, Fuencarral-El Pardo, Madrid, \u00c1rea metropolitana de Madrid y Corredor del Henares, Comunidad de Madrid, 28001, Espa\u00f1a", "40.48256135", "-3.69060790", "edu", ""], ["Adversarial Attack Type I: Generating False Positives", "", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification", "University of Maryland, College Park", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Grouper: Optimizing Crowdsourced Face Annotations", "Michigan State University", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization", "Faculty of Electrical Engineering, University of Ljubljana, Tr za ska cesta", "University of Ljubljana", "University of Ljubljana", "UL Fakulteta za ra\u010dunalni\u0161tvo in informatiko, 113, Ve\u010dna pot, Vrta\u010da, Ro\u017ena dolina, Ljubljana, Upravna Enota Ljubljana, Osrednjeslovenska, 1000, Slovenija", "46.05015580", "14.46907327", "edu", ""], ["Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition", "University of Groningen, The Netherlands", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["Learning the Face Prior for Bayesian Face Recognition", "The Chinese University of Hong Kong, China", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition", "", "Facebook", "Facebook", "250 Bryant St, Mountain View, CA 94041, USA", "37.39367170", "-122.08072620", "company", "Facebook, Mountain View, CA"], ["Context-Aware Local Binary Feature Learning for Face Recognition", "Department of Automation, State Key Lab of Intelligent Technologies and Systems and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China", "Tsinghua University", "Tsinghua University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Investigating Nuisances in DCNN-Based Face Recognition", "Department of Information Engineering, University of Florence, Florence, Italy", "University of Florence", "University of Florence", "Piazza di San Marco, 4, 50121 Firenze FI, Italy", "43.77764260", "11.25976500", "edu", ""], ["Non-parametric Bayesian Constrained Local Models", "Institute of Systems and Robotics, University of Coimbra, Portugal", "Institute of Systems and Robotics", "Institute of Systems and Robotics", "Institut f\u00fcr Robotik und Kognitive Systeme, 160, Ratzeburger Allee, Strecknitz, Sankt J\u00fcrgen, Strecknitz, L\u00fcbeck, Schleswig-Holstein, 23562, Deutschland", "53.83383710", "10.70359390", "edu", ""], ["Effective 3D based frontalization for unconstrained face recognition", "Media Integration and Communication Center - 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All rights reserved", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection", "Institute of Arti cial Intelligence and Cognitive Engineering (ALICE), University of Groningen", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["Elastic preserving projections based on L1-norm maximization", "School of Electronic and Information Engineering, Beihang University, Beijing, China", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["HyperFace: A Deep Multi-task Learning Framework for 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NY, USA", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Landmark Free Face Attribute Prediction", "School of Computing, National University of Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Multi-objective convolutional learning for face labeling", "UC Merced, USA", "University of California, Merced", "University of California, Merced", "University of California, Merced, Ansel Adams Road, Merced County, California, USA", "37.36566745", "-120.42158888", "edu", ""], ["Multi-Task Convolutional Neural Network for Face Recognition", "Michigan State University, East Lansing MI", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["One-to-many face recognition with bilinear CNNs", "University of Massachusetts, Amherst", "University of Massachusetts", "University of Massachusetts", "University of Massachusetts, Hicks Way, Amherst, Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["A Wearable IoT with Complex Artificial Perception Embedding for Alzheimer Patients", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering", "Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA", "33.58667840", "-101.87539204", "edu", ""], ["One Shot Similarity Metric Learning for Action Recognition", "The Weizmann Institute of", "Weizmann Institute of Science", "Weizmann Institute of Science", "\u05de\u05db\u05d5\u05df \u05d5\u05d9\u05e6\u05de\u05df 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Hampshire, Massachusetts, 01003, USA", "42.38897850", "-72.52869870", "edu", ""], ["Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes", "School of Computer Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Deep tree-structured face: A unified representation for multi-task facial biometrics", "University of Tennessee, Knoxville", "University of Tennessee", "University of Tennessee", "University of Tennessee, Melrose Avenue, Fort Sanders, Knoxville, Knox County, Tennessee, 37916, USA", "35.95424930", "-83.93073950", "edu", ""], ["Face recognition based on manifold constrained joint sparse sensing with K-SVD", "Xinjiang Vocational and Technical College of Communications, Wulumuqi, People\u2019s Republic of China", "Shanghai University", "Shanghai University", 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University", "\u6e05\u534e\u5927\u5b66, 30, \u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["The unconstrained ear recognition challenge", "University of Ljubljana", "University of Ljubljana", "University of Ljubljana", "UL Fakulteta za ra\u010dunalni\u0161tvo in informatiko, 113, Ve\u010dna pot, Vrta\u010da, Ro\u017ena dolina, Ljubljana, Upravna Enota Ljubljana, Osrednjeslovenska, 1000, Slovenija", "46.05015580", "14.46907327", "edu", ""], ["Efficient Annotation of Objects for Video Analysis", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology, Hyderabad, Campus Road, Ward 105 Gachibowli, Greater Hyderabad Municipal Corporation West Zone, Hyderabad, Rangareddy District, Telangana, 500032, India", "17.44549570", "78.34854698", "edu", ""], ["Attribute preserved face de-identification", "Department of Computer Science and Engineering, Michigan State University, East Lansing 48824, USA", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Locality Preserving Collaborative Representation for Face Recognition", "School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China", "Xiamen University", "Xiamen University", "\u53a6\u95e8\u5927\u5b66, \u601d\u660e\u5357\u8def Siming South Road, \u601d\u660e\u533a, \u601d\u660e\u533a (Siming), \u53a6\u95e8\u5e02 / Xiamen, \u798f\u5efa\u7701, 361005, \u4e2d\u56fd", "24.43994190", "118.09301781", "edu", ""], ["Deep Density Clustering of Unconstrained Faces", "University of Maryland, College Park", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca 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partial matching metric for face recognition", "", "Microsoft", "Microsoft Corporation, Redmond, WA, USA", "One Microsoft Way, Redmond, WA 98052, USA", "47.64233180", "-122.13693020", "company", ""], ["Fully automated facial picture evaluation using high level attributes", "GIPSA-Lab, Grenoble, France", "GIPSA-Lab, Grenoble, France", "GIPSA-Lab, Grenoble, France", "GIPSA-lab, 11, Rue des Math\u00e9matiques, M\u00e9diat Rh\u00f4ne-Alpes, Saint-Martin-d'H\u00e8res, Grenoble, Is\u00e8re, Auvergne-Rh\u00f4ne-Alpes, France m\u00e9tropolitaine, 38400, France", "45.19292450", "5.76619830", "edu", ""], ["Robust Face Recognition with Deep Multi-View Representation Learning", "National University of Singapore, Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Regressing Robust and Discriminative 3D Morphable Models with a 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\u53cc\u6e05\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100084, \u4e2d\u56fd", "40.00229045", "116.32098908", "edu", ""], ["Mean-Variance Loss for Deep Age Estimation from a Face", "University of Chinese Academy of Sciences, Beijing, 100049, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Rank Minimization across Appearance and Shape for AAM Ensemble Fitting", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology, Macgregor Lane, Merthyr, South Brisbane, Brisbane, QLD, 4000, Australia", "-27.47715625", "153.02841004", "edu", ""], ["Unsupervised Training for 3D Morphable Model Regression", "", "MIT CSAIL", "MIT CSAIL", "32 Vassar St, Cambridge, MA 02139, USA", "42.36194070", "-71.09043780", "edu", ""], ["A Novel Two-stage Learning Pipeline for Deep Neural Networks", "University of Science and Technology of China, Hefei, China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Toward Open-Set Face Recognition", "Vision and Security Technology Lab, University of Colorado Colorado Springs", "University of Colorado, Colorado Springs", "University of Colorado Colorado Springs", "Main Hall, The Spine, Colorado Springs, El Paso County, Colorado, 80907, USA", "38.89207560", "-104.79716389", "edu", ""], ["Face recognition in the wild with the Probabilistic Gabor-Fisher 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Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["A pseudo ensemble convolutional neural networks", "University of Science &amp; Technology (UST), Daejeon, Korea", "University of Science and Technology, Korea", "University of Science &amp; Technology (UST), Daejeon, Korea", "217 Gajeong-ro, Sinseong-dong, Yuseong-gu, Daejeon, South Korea", "36.38513950", "127.36834130", "edu", "University of Science and Technology, Daejeon, Korea"], ["L2-constrained Softmax Loss for Discriminative Face Verification", "Center for Automation Research, UMIACS, University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Multilayer Surface Albedo for Face Recognition With Reference Images in Bad Lighting Conditions", "Department of MathematicsIntelligent Data Center, Sun Yat-sen University, Guangzhou, China", "Sun Yat-Sen University", "Sun Yat-Sen University", "\u4e2d\u5927, \u65b0\u6e2f\u897f\u8def, \u9f99\u8239\u6ed8, \u5eb7\u4e50, \u6d77\u73e0\u533a (Haizhu), \u5e7f\u5dde\u5e02, \u5e7f\u4e1c\u7701, 510105, \u4e2d\u56fd", "23.09461185", "113.28788994", "edu", ""], ["A Community Detection Approach to Cleaning Extremely Large Face Database", "National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China", "National University of Defense Technology, China", "National University of Defence Technology, Changsha 410000, China", "\u56fd\u9632\u79d1\u5b66\u6280\u672f\u5927\u5b66, \u4e09\u4e00\u5927\u9053, \u5f00\u798f\u533a, \u5f00\u798f\u533a (Kaifu), \u957f\u6c99\u5e02 / Changsha, \u6e56\u5357\u7701, 410073, \u4e2d\u56fd", "28.22902090", "112.99483204", "edu", ""], ["Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition", "West Virginia University", "West Virginia University", "West Virginia University", "88, Windsor Avenue, The Flatts, Morgantown, Monongalia County, West Virginia, 26505, USA", "39.65404635", "-79.96475355", "edu", ""], ["Exploring computation-communication tradeoffs in camera systems", "University of Washington", "University of Washington", "University of Washington", "University of Washington, Rainier Vista, Montlake, University District, Seattle, King County, Washington, 98195, USA", "47.65432380", "-122.30800894", "edu", ""], ["Making a Science of Model Search", "Rowland Institute at Harvard", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["Simultaneous Cascaded Regression", "", "Institute of Systems and Robotics", "Institute of Systems and Robotics", "Institut f\u00fcr Robotik und Kognitive Systeme, 160, Ratzeburger Allee, Strecknitz, Sankt J\u00fcrgen, Strecknitz, L\u00fcbeck, Schleswig-Holstein, 23562, Deutschland", "53.83383710", "10.70359390", "edu", ""], ["Learning Discriminative Features with Class Encoder", "University of Chinese Academy of Science", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features", "Department of Electronic EngineeringCentre for Vision, Speech and Signal Processing, University of Surrey, Surrey, U.K.", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation", "University of Science and Technology of China", "University of Science and Technology of China", "University of Science and Technology of China", "\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u4e1c\u6821\u533a, 96\u53f7, \u91d1\u5be8\u8def, \u6c5f\u6dee\u5316\u80a5\u5382\u5c0f\u533a, \u829c\u6e56\u8def\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230026, \u4e2d\u56fd", "31.83907195", "117.26420748", "edu", ""], ["Training Deep Face Recognition Systems with Synthetic Data", "University of Basel", "University of Basel", "University of Basel", "Faculty of Psychology, University of Basel, 60-62, Missionsstrasse, Grossbasel, Am Ring, Basel, Basel-Stadt, 4055, Schweiz/Suisse/Svizzera/Svizra", "47.56126510", "7.57529610", "edu", ""], ["Joint Face Alignment and 3D Face Reconstruction", "Michigan State University, East Lansing, MI, U.S.A", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition", "Shenzhen Key Lab of Comp. Vis. and Pat. Rec., Shenzhen Institutes of Advanced Technology, CAS, China", "Shenzhen Institutes of Advanced Technology", "Shenzhen Institutes of Advanced Technology", "\u4e2d\u56fd\u79d1\u5b66\u9662\u6df1\u5733\u5148\u8fdb\u6280\u672f\u7814\u7a76\u9662, 1068, \u79d1\u7814\u8def, \u6df1\u5733\u5927\u5b66\u57ce, \u4e09\u5751\u6751, \u5357\u5c71\u533a, \u6df1\u5733\u5e02, \u5e7f\u4e1c\u7701, 518000, \u4e2d\u56fd", "22.59805605", "113.98533784", "edu", ""], ["Weakly Supervised Facial Analysis with Dense Hyper-Column Features", "Carnegie Mellon University, Pittsburgh, PA, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Partial face detection for continuous authentication", "UMIACS, University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["An Efficient Joint Formulation for Bayesian Face Verification", "Visual Computing Group, Microsoft Research, Beijing, China", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Complexity of multiverse networks and their multilayer generalization", "Tel Aviv University", "Tel Aviv University", "Tel Aviv University", "\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05ea \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, \u05db\u05d9\u05db\u05e8 \u05de\u05e0\u05d3\u05dc, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1 - \u05d9\u05e4\u05d5, \u05d0\u05e4\u05e7\u05d4, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1-\u05d9\u05e4\u05d5, \u05de\u05d7\u05d5\u05d6 \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.11198890", "34.80459702", "edu", ""], ["Randomized Intraclass-Distance Minimizing Binary Codes for face recognition", "National Institute of Standards and Technology", "National Institute of Standards and Technology", "National Institute of Standards and Technology", "National Institute of Standards and Technology, Summer Walk Drive, Diamond Farms, Gaithersburg, Montgomery County, Maryland, 20878, USA", "39.12549380", "-77.22293475", "edu", ""], ["Evolutionary Cost-Sensitive Discriminative Learning With Application to Vision and Olfaction", "Department of Computing, The Hong Kong Polytechnic University, Hong Kong", "Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "hong kong, 11, \u80b2\u624d\u9053 Yuk Choi Road, \u5c16\u6c99\u5480 Tsim Sha Tsui, \u6cb9\u5c16\u65fa\u5340 Yau Tsim Mong District, \u4e5d\u9f8d Kowloon, HK, 00000, \u4e2d\u56fd", "22.30457200", "114.17976285", "edu", ""], ["RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models", "University of Twente", "University of Twente", "University of Twente", "University of Twente, De Achterhorst;Hallenweg, Enschede, Regio Twente, Overijssel, Nederland, 7522NH, Nederland", "52.23801390", "6.85667610", "edu", ""], ["Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets", "University of Wisconsin   Madison", "University of Wisconsin Madison", "University of Wisconsin Madison", "University of Wisconsin-Madison, Marsh Lane, Madison, Dane County, Wisconsin, 53705-2221, USA", "43.07982815", "-89.43066425", "edu", ""], ["Generating a Large, Freely-Available Dataset for Face-Related Algorithms", "Amherst College", "Amherst College", "Amherst College", "Amherst College, Boltwood Avenue, Amherst, Hampshire, Massachusetts, 01004, USA", "42.37289000", "-72.51881400", "edu", ""], ["Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning", "Department of Electrical and Computer Engineering, College of the Computer and Information Science, Northeastern University, Boston, MA, USA", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Markov Chain Monte Carlo for Automated Face Image Analysis", "Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland", "University of Basel", "University of Basel", "Faculty of Psychology, University of Basel, 60-62, Missionsstrasse, Grossbasel, Am Ring, Basel, Basel-Stadt, 4055, Schweiz/Suisse/Svizzera/Svizra", "47.56126510", "7.57529610", "edu", ""], ["Local Large-Margin Multi-Metric Learning for Face and Kinship Verification", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis", "University of Chinese Academy of Sciences, Beijing, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["Exploring the geo-dependence of human face appearance", "University of Kentucky", "University of Kentucky", "University of Kentucky", "University of Kentucky, Columbia Avenue, Sorority Circle, Lexington, Fayette County, Kentucky, 40508, USA", "38.03337420", "-84.50177580", "edu", ""], ["On the Applications of Robust PCA in Image and Video Processing", "Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Sparsely Encoded Local Descriptor for face recognition", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Chinese Academy of Science", "Key Lab of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Science (CAS), Beijing, 100190, China", "Beijing, China", "39.90419990", "116.40739630", "edu", ""], ["Inference Attacks Against Collaborative Learning", "UCL and Alan Turing Institute", "Cornell University", "Cornell University", "Cornell University, Forest Home Drive, Forest Home, Tompkins County, New York, 14853, USA", "42.45055070", "-76.47835130", "edu", ""], ["Deep Transfer Metric Learning", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. 2Advanced Digital Sciences Center, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Towards Pose-robust Face Recognition on Video", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology", "Queensland University of Technology, Macgregor Lane, Merthyr, South Brisbane, Brisbane, QLD, 4000, Australia", "-27.47715625", "153.02841004", "edu", ""], ["Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling", "University of California at Berkeley / ICSI", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Learning Symmetry Features for Face Detection Based on Sparse Group Lasso", "National Laboratory of Pattern Recognition, Institute of Automation", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Constructing Local Binary Pattern Statistics by Soft Voting", "", "University of Oulu", "University of Oulu", "Oulun yliopisto, Biologintie, Linnanmaa, Oulu, Oulun seutukunta, Pohjois-Pohjanmaa, Pohjois-Suomen aluehallintovirasto, Pohjois-Suomi, Manner-Suomi, 90540, Suomi", "65.05921570", "25.46632601", "edu", ""], ["Learning Structured Output Representation using Deep Conditional Generative Models", "University of Michigan, Ann Arbor", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Online, Real-Time Tracking Using a Category-to-Individual Detector", "California Institute of Technology, USA", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["High-Order Local Spatial Context Modeling by Spatialized Random Forest", "Hefei University of Technology, Hefei, China", "Hefei University of Technology", "Hefei University of Technology", "\u5408\u80a5\u5de5\u4e1a\u5927\u5b66\uff08\u5c6f\u6eaa\u8def\u6821\u533a\uff09, 193\u53f7, \u5357\u4e00\u73af\u8def, \u822a\u8fd0\u5357\u6751, \u5305\u516c\u8857\u9053, \u5408\u80a5\u5e02\u533a, \u5408\u80a5\u5e02, \u5b89\u5fbd\u7701, 230009, \u4e2d\u56fd", "31.84691800", "117.29053367", "edu", ""], ["Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures", "Rowland Institute at Harvard", "MIT", "Massachusetts Institute", "MIT, Amherst Street, Cambridgeport, Cambridge, Middlesex County, Massachusetts, 02238, USA", "42.35839610", "-71.09567788", "edu", ""], ["Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Learning Face Image Quality From Human Assessments", "Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["VIPLFaceNet: an open source deep face recognition SDK", "University of Chinese Academy of Sciences, Beijing 100049, China", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["On-line machine learning accelerator on digital RRAM-crossbar", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Hierarchical-PEP model for real-world face recognition", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology", "Stevens Institute of Technology, River Terrace, Hoboken, Hudson County, New Jersey, 07030, USA", "40.74225200", "-74.02709490", "edu", ""], ["Displacement Template with Divide-&-Conquer Algorithm for Significantly Improving Descriptor Based Face Recognition Approaches", "Wenzhou University, China", "University of Northern British Columbia", "University of Northern British Columbia", "UNBC, Campus Ring Road, College Heights, Prince George, Regional District of Fraser-Fort George, British Columbia, V2M 5K7, Canada", "53.89256620", "-122.81471592", "edu", ""], ["Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization", "The Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "Hong Kong Polytechnic University", "hong kong, 11, \u80b2\u624d\u9053 Yuk Choi Road, \u5c16\u6c99\u5480 Tsim Sha Tsui, \u6cb9\u5c16\u65fa\u5340 Yau Tsim Mong District, \u4e5d\u9f8d Kowloon, HK, 00000, \u4e2d\u56fd", "22.30457200", "114.17976285", "edu", ""], ["Selective Encoding for Recognizing Unreliably Localized Faces", "University of Maryland, College Park, MD", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["Report on the FG 2015 Video Person Recognition Evaluation", "University of Technology, Sydney", "University of Technology Sydney", "University of Technology Sydney", "University of Technology Sydney, Omnibus Lane, Ultimo, Sydney, NSW, 2007, Australia", "-33.88096510", "151.20107299", "edu", ""], ["Face Verification with Multi-Task and Multi-Scale Features Fusion", "", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Variational Relevance Vector Machine for Tabular Data", "The Blavatnik School of Computer Science, The Tel-Aviv University", "Tel Aviv University", "Tel Aviv University", "\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05ea \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, \u05db\u05d9\u05db\u05e8 \u05de\u05e0\u05d3\u05dc, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1 - \u05d9\u05e4\u05d5, \u05d0\u05e4\u05e7\u05d4, \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1-\u05d9\u05e4\u05d5, \u05de\u05d7\u05d5\u05d6 \u05ea\u05dc \u05d0\u05d1\u05d9\u05d1, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.11198890", "34.80459702", "edu", ""], ["Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition", "School of Engineering, Griffith University, Nathan, QLD, Australia", "Griffith University", "Griffith University", "Griffith University Nathan Campus, Johnson Path, Nathan, Nathan Heights, QLD, 4111, Australia", "-27.55339750", "153.05336234", "edu", ""], ["Learning Structure and Strength of CNN Filters for Small Sample Size Training", "", "Indian Institute of Technology Delhi", "IIIT-Delhi, India", "IIIT-Delhi, Mathura Road, Friends Colony, South East Delhi, Delhi, 110020, India", "28.54632595", "77.27325504", "edu", ""], ["Robust Point Set Matching for Partial Face Recognition", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "Nanyang Technological University", "Nanyang Technological University", "NTU, Faculty Avenue, Jurong West, Southwest, 637460, Singapore", "1.34841040", "103.68297965", "edu", ""], ["Learning structured sparse representation for single sample face recognition", "School of Electrical Engineering, Nantong University, Nantong, China", "Nantong University", "Nantong University", "\u5357\u901a\u5927\u5b66, \u72fc\u5c71\u9547\u8857\u9053, \u5d07\u5ddd\u533a (Chongchuan), \u5357\u901a\u5e02 / Nantong, \u6c5f\u82cf\u7701, 226000, \u4e2d\u56fd", "31.97474630", "120.90779264", "edu", ""], ["Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning", "College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China", "Zhejiang University of Technology", "Zhejiang University of Technology", "\u6d59\u6c5f\u5de5\u4e1a\u5927\u5b66, \u6f6e\u738b\u8def, \u671d\u6656\u8857\u9053, \u676d\u5dde\u5e02 Hangzhou, \u6d59\u6c5f\u7701, 310014, \u4e2d\u56fd", "30.29315340", "120.16204580", "edu", ""], ["Unconstrained face recognition: Establishing baseline human performance via crowdsourcing", "Michigan State University, East Lansing, MI, U.S.A", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["AgeDB: The First Manually Collected, In-the-Wild Age Database", "Middlesex University London", "Middlesex University", "Middlesex University", "Middlesex University, Greyhound Hill, Hendon, The Hyde, London Borough of Barnet, London, Greater London, England, NW4 4JP, UK", "51.59029705", "-0.22963221", "edu", ""], ["Applying scattering operators for face recognition: A comparative study", "", "National Taiwan University", "National Taiwan University", "\u81fa\u5927;\u53f0\u5927, 1, \u7f85\u65af\u798f\u8def\u56db\u6bb5, \u5b78\u5e9c\u91cc, \u5927\u5b89\u5340, \u81fa\u5317\u5e02, 10617, \u81fa\u7063", "25.01682835", "121.53846924", "edu", ""], ["CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval", "University of Caen, France", "University of Caen", "University of Caen", "\u4eac\u90fd\u5927\u5b66, \u4eca\u51fa\u5ddd\u901a, \u5409\u7530\u6cc9\u6bbf\u753a, \u5de6\u4eac\u533a, \u4eac\u90fd\u5e02, \u4eac\u90fd\u5e9c, \u8fd1\u757f\u5730\u65b9, 606-8501, \u65e5\u672c", "35.02749960", "135.78154513", "edu", ""], ["Faceless Person Recognition; Privacy Implications in Social Media", "Max-Planck Institute for Informatics", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["Deep Multi-Center Learning for Face Alignment", "Shanghai Jiao Tong University, China", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Discriminative Elastic-Net Regularized Linear Regression", "School of Computing Sciences, University of East Anglia, Norwich, U.K.", "University of East Anglia", "University of East Anglia", "Arts (Lower Walkway Level), The Square, Westfield View, Earlham, Norwich, Norfolk, East of England, England, NR4 7TJ, UK", "52.62215710", "1.24091360", "edu", ""], ["Deep Convolutional Network Cascade for Facial Point Detection", "The Chinese University of Hong Kong", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Teaching Computer Vision: Bringing Research Benchmarks to the Classroom", "The Open University of Israel, Israel", "Open University of Israel", "Open University of Israel", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 \u05d4\u05e4\u05ea\u05d5\u05d7\u05d4, 15, \u05d0\u05d1\u05d0 \u05d7\u05d5\u05e9\u05d9, \u05d7\u05d9\u05e4\u05d4, \u05d2\u05d1\u05e2\u05ea \u05d3\u05d0\u05d5\u05e0\u05e1, \u05d7\u05d9\u05e4\u05d4, \u05de\u05d7\u05d5\u05d6 \u05d7\u05d9\u05e4\u05d4, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.77824165", "34.99565673", "edu", ""], ["Kinship Verification through Transfer Learning", "and Southeast University, China", "SUNY Buffalo", "SUNY Buffalo", "SUNY College at Buffalo, Academic Drive, Elmwood Village, Buffalo, Erie County, New York, 14222, USA", "42.93362780", "-78.88394479", "edu", ""], ["Quality Aware Network for Set to Set Recognition", "University of Sydney", "University of Sydney", "University of Sydney", "USyd, Fisher Road, Camperdown, Sydney, NSW, 2006, Australia", "-33.88890695", "151.18943366", "edu", ""], ["Is block matching an alternative tool to LBP for face recognition?", "KTH Royal Institute of Technology, Stockholm, Sweden", "KTH Royal Institute of Technology, Stockholm", "KTH   Royal Institute of Technology, Stockholm", "KTH, Teknikringen, L\u00e4rkstaden, Norra Djurg\u00e5rden, \u00d6stermalms stadsdelsomr\u00e5de, Sthlm, Stockholm, Stockholms l\u00e4n, Svealand, 114 28, Sverige", "59.34986645", "18.07063213", "edu", ""], ["4D unconstrained real-time face recognition using a commodity depth camera", "University of Groningen, Nijenborgh 9, 9747 AG, The Netherlands", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["V-shaped interval insensitive loss for ordinal classification", "Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czech Republic", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed m\u011bsto Praha, Hlavn\u00ed m\u011bsto Praha, Praha, 11121, \u010cesko", "50.07642960", "14.41802312", "edu", ""], ["Learning One-Shot Exemplar SVM from the Web for Face Verification", "", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Boosting Pseudo Census Transform Features for Face Alignment", "", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", ""], ["FaceTracer: A Search Engine for Large Collections of Images with Faces", "Columbia University", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples", "", "Purdue University", "Purdue University", "Purdue University, West Stadium Avenue, West Lafayette, Tippecanoe County, Indiana, 47907, USA", "40.43197220", "-86.92389368", "edu", ""], ["A talking profile to distinguish identical twins", "School of Computing, National University of Singapore, Singapore", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Active Rare Class Discovery and Classification Using Dirichlet Processes", "School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", ""], ["A Critical Review of Action Recognition Benchmarks", "The Open University of Israel", "Open University of Israel", "Open University of Israel", "\u05d4\u05d0\u05d5\u05e0\u05d9\u05d1\u05e8\u05e1\u05d9\u05d8\u05d4 \u05d4\u05e4\u05ea\u05d5\u05d7\u05d4, 15, \u05d0\u05d1\u05d0 \u05d7\u05d5\u05e9\u05d9, \u05d7\u05d9\u05e4\u05d4, \u05d2\u05d1\u05e2\u05ea \u05d3\u05d0\u05d5\u05e0\u05e1, \u05d7\u05d9\u05e4\u05d4, \u05de\u05d7\u05d5\u05d6 \u05d7\u05d9\u05e4\u05d4, NO, \u05d9\u05e9\u05e8\u05d0\u05dc", "32.77824165", "34.99565673", "edu", ""], ["Utility 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new file mode 100644
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diff --git a/site/datasets/citations/670637d0303a863c1548d5b19f705860a23e285c.json b/site/datasets/citations/670637d0303a863c1548d5b19f705860a23e285c.json
new file mode 100644
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East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Learning a sequential search for landmarks", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["A Latent Clothing Attribute Approach for Human Pose Estimation", "", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""], ["Creating Picture Legends for Group Photos", "", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Efficient Annotation of Objects for Video Analysis", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology, Hyderabad, Campus Road, Ward 105 Gachibowli, Greater Hyderabad Municipal Corporation West Zone, Hyderabad, Rangareddy District, Telangana, 500032, India", "17.44549570", "78.34854698", "edu", ""], ["Non-rigid object tracking in video sequences", "", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed m\u011bsto Praha, Hlavn\u00ed m\u011bsto Praha, Praha, 11121, \u010cesko", "50.07642960", "14.41802312", "edu", ""], ["Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery", "", "University of Manchester", "University of Manchester", "University of Manchester - Main Campus, Brunswick Street, Curry Mile, Ardwick, Manchester, Greater Manchester, North West England, England, M13 9NR, UK", "53.46600455", "-2.23300881", "edu", ""], ["Finding Human Poses in Videos Using Concurrent Matching and Segmentation", "", "Boston College", "Boston College", "Boston College, 140, Commonwealth Avenue, Chestnut Hill, Newton, Middlesex County, Massachusetts, 02467, USA", "42.33544810", "-71.16813864", "edu", ""], ["Lying Pose Recognition for Elderly Fall Detection", "", "RWTH Aachen University", "RWTH Aachen University", "RWTH Aachen, Mies-van-der-Rohe-Stra\u00dfe, K\u00f6nigsh\u00fcgel, Aachen-Mitte, Aachen, St\u00e4dteregion Aachen, Regierungsbezirk K\u00f6ln, Nordrhein-Westfalen, 52074, Deutschland", "50.77917030", "6.06728733", "edu", ""], ["Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation", "", "Simon Fraser University", "Simon Fraser University", "SFU Burnaby, South Campus Road, Barnet, Burnaby, Metro Vancouver, British Columbia, V5A 4X6, Canada", "49.27674540", "-122.91777375", "edu", ""], ["Part-based Deformable Object Detection with a Single Silhouette Sketch", "", "Indian Institute of Technology Delhi", "IIIT-Delhi, India", "IIIT-Delhi, Mathura Road, Friends Colony, South East Delhi, Delhi, 110020, India", "28.54632595", "77.27325504", "edu", ""], ["Stacked Hourglass Networks for Human Pose Estimation", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Grouplet: A structured image representation for recognizing human and object interactions", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Cascaded pose regression", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation", "", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", "edu", ""], ["Implicit models for automatic pose estimation in static images", "", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Human Pose Estimation Using Body Parts Dependent Joint Regressors", "", "ETH Z\u00fcrich", "ETH Z\u00fcrich", "ETH Z\u00fcrich, 101, R\u00e4mistrasse, Hochschulen, Altstadt, Z\u00fcrich, Bezirk Z\u00fcrich, Z\u00fcrich, 8092, Schweiz/Suisse/Svizzera/Svizra", "47.37645340", "8.54770931", "edu", ""], ["Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation", "", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["Seeing 3D objects in a single 2D image", "", "University of Minnesota", "University of Minnesota", "WeismanArt, 333, East River Parkway, Marcy-Holmes, Phillips, Minneapolis, Hennepin County, Minnesota, 55455, USA", "44.97308605", "-93.23708813", "edu", ""], ["Estimating 3D Human Pose from Single Images Using Iterative Refinement of the Prior", "", "Swansea University", "Swansea University", "Swansea University, University Footbridge, Sketty, Swansea, Wales, SA2 8PZ, UK", "51.60915780", "-3.97934429", "edu", ""], ["Recognizing Degraded Handwritten Characters", "", "Vienna University of Technology", "Vienna University of Technology", "TU Wien, Hauptgeb\u00e4ude, Hoftrakt, Freihausviertel, KG Wieden, Wieden, Wien, 1040, \u00d6sterreich", "48.19853965", "16.36986168", "edu", ""], ["Sputnik Tracker: Having a Companion Improves Robustness of the Tracker", "", "Czech Technical University", "Czech Technical University", "\u010cesk\u00e9 vysok\u00e9 u\u010den\u00ed technick\u00e9 v Praze, Resslova, Nov\u00e9 M\u011bsto, Praha, okres Hlavn\u00ed m\u011bsto Praha, Hlavn\u00ed m\u011bsto Praha, Praha, 11121, \u010cesko", "50.07642960", "14.41802312", "edu", ""], ["Regularity Guaranteed Human Pose Correction", "", "Shanghai University", "Shanghai University", "\u4e0a\u6d77\u5927\u5b66, \u9526\u79cb\u8def, \u5927\u573a\u9547, \u5b9d\u5c71\u533a (Baoshan), \u4e0a\u6d77\u5e02, 201906, \u4e2d\u56fd", "31.32235655", "121.38400941", "edu", ""], ["Joint Deep Learning for Pedestrian Detection", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["Recognizing actions from still images", "", "Bilkent University", "Bilkent University", "Bilkent \u00dcniversitesi, 3. Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Modeling Instance Appearance for Recognition \u2013 Can We Do Better Than EM?", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Pose search: Retrieving people using their pose", "", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Easy Minimax Estimation with Random Forests for Human Pose Estimation", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Skeleton Search: Category-Specific Object Recognition and\u00a0Segmentation Using a Skeletal Shape Model", "", "Brown University", "Brown University", "Brown University, Waterman Street, College Hill, Providence, Bristol, Rhode Island, 02912, USA", "41.82686820", "-71.40123146", "edu", ""], ["Vision-Based Fallen Person Detection for the Elderly", "", "York University", "York University", "York University, Keele Campus, Campus Walk, North York, Toronto, Ontario, M3J 2S5, Canada", "43.77439110", "-79.50481085", "edu", ""], ["Inner Space Preserving Generative Pose Machine", "", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos", "", "ETH Zurich", "Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland", "Sternwartstrasse 7, 8092 Z\u00fcrich, Switzerland", "47.37723980", "8.55216180", "edu", ""], ["A Complete View Depended Volleyball Video Dataset under the Uncontrolled Conditions", "", "Islamic Azad University", "Islamic Azad University", "\u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u0622\u0632\u0627\u062f \u0627\u0633\u0644\u0627\u0645\u06cc, \u0647\u0645\u062f\u0627\u0646, \u0628\u062e\u0634 \u0645\u0631\u06a9\u0632\u06cc \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0627\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u200f\u0627\u06cc\u0631\u0627\u0646\u200e", "34.84529990", "48.55962120", "edu", ""], ["Recognizing human actions from still images with latent poses", "", "Simon Fraser University", "Simon Fraser University", "SFU Burnaby, South Campus Road, Barnet, Burnaby, Metro Vancouver, British Columbia, V5A 4X6, Canada", "49.27674540", "-122.91777375", "edu", ""], ["Probabilistic sequence models for image sequence processing and recognition", "", "RWTH Aachen University", "RWTH Aachen University", "RWTH Aachen, Mies-van-der-Rohe-Stra\u00dfe, K\u00f6nigsh\u00fcgel, Aachen-Mitte, Aachen, St\u00e4dteregion Aachen, Regierungsbezirk K\u00f6ln, Nordrhein-Westfalen, 52074, Deutschland", "50.77917030", "6.06728733", "edu", ""], ["Predicting People's 3D Poses from Short Sequences", "", "Graz University of Technology", "Graz University of Technology", "TU Graz, Inffeldgasse, Harmsdorf, Jakomini, Graz, Steiermark, 8010, \u00d6sterreich", "47.05821000", "15.46019568", "edu", ""], ["Learning Kinematic Models for Articulated Objects", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Shape Models of the Human Body for Distributed Inference", "", "Brown University", "Brown University", "Brown University, Waterman Street, College Hill, Providence, Bristol, Rhode Island, 02912, USA", "41.82686820", "-71.40123146", "edu", ""], ["Has My Algorithm Succeeded? An Evaluator for Human Pose Estimators", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Recognizing objects by piecing together the Segmentation Puzzle", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Beyond Controllers Human Segmentation, Pose, and Depth Estimation as Game Input Mechanisms", "", "Oxford Brookes University", "Oxford Brookes University", "Oxford Brookes University, Headington Road, Headington, Oxford, Oxon, South East, England, OX3 0BL, UK", "51.75552050", "-1.22615970", "edu", ""], ["Extracting Moving People from Internet Videos", "", "Princeton University", "Princeton University", "Lot 9, University Place, Princeton Township, Mercer County, New Jersey, 08540, USA", "40.34829285", "-74.66308325", "edu", ""], ["Surveillance Video Analysis with External Knowledge and Internal Constraints", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["HUMAN POSE ESTIMATION FROM A SINGLE VIEW POINT by", "", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", "edu", ""], ["Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["A Heuristic Deformable Pedestrian Detection Method", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Multi-Scale Human Pose Tracking in 2D Monocular Images", "", "Curtin University", "Curtin University", "Curtin University, Brand Drive, Waterford, Perth, Western Australia, 6102, Australia", "-32.00686365", "115.89691775", "edu", ""], ["Finding People Using Scale, Rotation and Articulation Invariant Matching", "", "Boston College", "Boston College", "Boston College, 140, Commonwealth Avenue, Chestnut Hill, Newton, Middlesex County, Massachusetts, 02467, USA", "42.33544810", "-71.16813864", "edu", ""], ["Training Object Class Detectors with Click Supervision", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Video retrieval by mimicking poses", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Shape-based pedestrian parsing", "", "Beijing Jiaotong University", "Beijing Jiaotong University", "\u5317\u4eac\u4ea4\u901a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100044, \u4e2d\u56fd", "39.94976005", "116.33629046", "edu", ""], ["ChaLearn Looking at People Challenge 2014: Dataset and Results", "", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["2D and 3D Pose Recovery from a Single Uncalibrated Video - A View and Activity Independent Framework", "", "Kingston University", "Kingston University", "Kingston University, Kingston Hill, Kingston Vale, Kingston-upon-Thames, London, Greater London, England, KT2 7TF, UK", "51.42930860", "-0.26840440", "edu", ""], ["A Unified Contour-Pixel Model for Figure-Ground Segmentation", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Efficient and Exact MAP-MRF Inference using Branch and Bound", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Gamesourcing to acquire labeled human pose estimation data", "", "University of North Carolina at Charlotte", "University of North Carolina at Charlotte", "Lot 20, Poplar Terrace Drive, Charlotte, Mecklenburg County, North Carolina, 28223, USA", "35.31034410", "-80.73261617", "edu", ""], ["A Hierarchical Model of Shape and Appearance for Human Action Classification", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["R-CNNs for Pose Estimation and Action Detection", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["Chained Predictions Using Convolutional Neural Networks", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["It's All About the Data", "Computer Science, U.Illinois at Urbana Champaign, Urbana, United States", "University of Illinois, Urbana Champaign", "Computer Science, U.Illinois at Urbana Champaign, Urbana, United States", "Champaign, IL, USA", "40.10195230", "-88.22716150", "edu", ""], ["Localizing Objects While Learning Their Appearance", "", "ETH Zurich", "Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland", "Sternwartstrasse 7, 8092 Z\u00fcrich, Switzerland", "47.37723980", "8.55216180", "edu", ""], ["Fast Energy Minimization Using Learned State Filters", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Integration of Local Image Cues for Probabilistic 2D Pose Recovery", "", "Kingston University", "Kingston University", "Kingston University, Kingston Hill, Kingston Vale, Kingston-upon-Thames, London, Greater London, England, KT2 7TF, UK", "51.42930860", "-0.26840440", "edu", ""], ["Beyond KernelBoost", "", "ETH Z\u00fcrich", "ETH Z\u00fcrich", "ETH Z\u00fcrich, 101, R\u00e4mistrasse, Hochschulen, Altstadt, Z\u00fcrich, Bezirk Z\u00fcrich, Z\u00fcrich, 8092, Schweiz/Suisse/Svizzera/Svizra", "47.37645340", "8.54770931", "edu", ""], ["Max Margin AND/OR Graph learning for parsing the human body", "", "University of California, Los Angeles", "University of California, Los Angeles", "200 UCLA, Medical Plaza Driveway Suite 540, Los Angeles, CA 90095, USA", "34.06877880", "-118.44500940", "edu", ""], ["Understanding Human Motion : Recognition and Retrieval of Human Activities", "", "Bilkent University", "Bilkent University", "Bilkent \u00dcniversitesi, 3. Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Improved Human Parsing with a Full Relational Model", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Human Pose Estimation for Multiple Frames", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Human Pose Estimation : Extension and Application", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology, Hyderabad, Campus Road, Ward 105 Gachibowli, Greater Hyderabad Municipal Corporation West Zone, Hyderabad, Rangareddy District, Telangana, 500032, India", "17.44549570", "78.34854698", "edu", ""], ["Automatic learning of British Sign Language from signed TV broadcasts", "", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Spatio-Temporal GrabCut human segmentation for face and pose recovery", "University of Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain", "University of Barcelona", "University of Barcelona", "Universitat de Barcelona, Carrer de la Diputaci\u00f3, l'Antiga Esquerra de l'Eixample, Eixample, Barcelona, BCN, CAT, 08013, Espa\u00f1a", "41.38689130", "2.16352385", "edu", ""], ["Video Monitoring and Analysis of Human Behavior for Diagnosis of Obstructive Sleep Apnoea", "", "University of Lincoln", "University of Lincoln", "University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["3D Human Pose Estimation from Monocular Image Sequences", "", "Aalborg University", "Aalborg University", "AAU, Pontoppidanstr\u00e6de, S\u00f8nder Tranders, Aalborg, Aalborg Kommune, Region Nordjylland, 9220, Danmark", "57.01590275", "9.97532827", "edu", ""], ["Unsupervised Video Adaptation for Parsing Human Motion", "", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Using Models of Objects with Deformable Parts for Joint Categorization and Segmentation of Objects", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["Towards Understanding Articulated Objects", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Appearance Descriptors for Person Re-identification: a Comprehensive Review", "", "University of Cagliari", "University of Cagliari, Italy", "Via Universit\u00e0, 40, 09124 Cagliari CA, Italy", "39.21736570", "9.11492180", "edu", ""], ["Human Pose Estimation from Monocular Images: A Comprehensive Survey", "", "Shandong University", "Shandong University", "\u5c71\u4e1c\u5927\u5b66, \u6cf0\u5b89\u8857, \u9ccc\u5c71\u536b\u8857\u9053, \u5373\u58a8\u533a, \u9752\u5c9b\u5e02, \u5c71\u4e1c\u7701, 266200, \u4e2d\u56fd", "36.36934730", "120.67381800", "edu", ""], ["Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice", "York University, Toronto, ON, Canada", "York University", "York University", "York University, Keele Campus, Campus Walk, North York, Toronto, Ontario, M3J 2S5, Canada", "43.77439110", "-79.50481085", "edu", ""], ["Metadata of the chapter that will be visualized in SpringerLink", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Holistic Shape-Based Object Recognition Using Bottom-Up Image Structures", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Static Pose Estimation from Depth Images using Random Regression Forests and Hough Voting", "", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Utility data annotation with Amazon Mechanical Turk", 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Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Modeling Instance Appearance for Recognition \u2013 Can We Do Better Than EM?", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Pose search: Retrieving people using their pose", "", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Easy Minimax Estimation with Random Forests for Human Pose Estimation", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Skeleton Search: Category-Specific Object Recognition and\u00a0Segmentation Using a Skeletal Shape Model", "", "Brown University", "Brown University", "Brown University, Waterman Street, College Hill, Providence, Bristol, Rhode Island, 02912, USA", "41.82686820", "-71.40123146", "edu", ""], ["Vision-Based Fallen Person Detection for the Elderly", "", "York University", "York University", "York University, Keele Campus, Campus Walk, North York, Toronto, Ontario, M3J 2S5, Canada", "43.77439110", "-79.50481085", "edu", ""], ["Inner Space Preserving Generative Pose Machine", "", "Northeastern University", "Northeastern University", "Snell Library, 360, Huntington Avenue, Roxbury Crossing, Fenway, Boston, Suffolk County, Massachusetts, 02115, USA", "42.33836680", "-71.08793524", "edu", ""], ["Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos", "", "ETH Zurich", "Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland", "Sternwartstrasse 7, 8092 Z\u00fcrich, Switzerland", "47.37723980", "8.55216180", "edu", ""], ["A Complete View Depended Volleyball Video Dataset under the Uncontrolled Conditions", "", "Islamic Azad University", "Islamic Azad University", "\u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u0622\u0632\u0627\u062f \u0627\u0633\u0644\u0627\u0645\u06cc, \u0647\u0645\u062f\u0627\u0646, \u0628\u062e\u0634 \u0645\u0631\u06a9\u0632\u06cc \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0634\u0647\u0631\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u0627\u0633\u062a\u0627\u0646 \u0647\u0645\u062f\u0627\u0646, \u200f\u0627\u06cc\u0631\u0627\u0646\u200e", "34.84529990", "48.55962120", "edu", ""], ["Recognizing human actions from still images with latent poses", "", "Simon Fraser University", "Simon Fraser University", "SFU Burnaby, South Campus Road, Barnet, Burnaby, Metro Vancouver, British Columbia, V5A 4X6, Canada", "49.27674540", "-122.91777375", "edu", ""], ["Probabilistic sequence models for image sequence processing and recognition", "", "RWTH Aachen University", "RWTH Aachen University", "RWTH Aachen, Mies-van-der-Rohe-Stra\u00dfe, K\u00f6nigsh\u00fcgel, Aachen-Mitte, Aachen, St\u00e4dteregion Aachen, Regierungsbezirk K\u00f6ln, Nordrhein-Westfalen, 52074, Deutschland", "50.77917030", "6.06728733", "edu", ""], ["Predicting People's 3D Poses from Short Sequences", "", "Graz University of Technology", "Graz University of Technology", "TU Graz, Inffeldgasse, Harmsdorf, Jakomini, Graz, Steiermark, 8010, \u00d6sterreich", "47.05821000", "15.46019568", "edu", ""], ["Learning Kinematic Models for Articulated Objects", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Shape Models of the Human Body for Distributed Inference", "", "Brown University", "Brown University", "Brown University, Waterman Street, College Hill, Providence, Bristol, Rhode Island, 02912, USA", "41.82686820", "-71.40123146", "edu", ""], ["Has My Algorithm Succeeded? An Evaluator for Human Pose Estimators", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Recognizing objects by piecing together the Segmentation Puzzle", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Beyond Controllers Human Segmentation, Pose, and Depth Estimation as Game Input Mechanisms", "", "Oxford Brookes University", "Oxford Brookes University", "Oxford Brookes University, Headington Road, Headington, Oxford, Oxon, South East, England, OX3 0BL, UK", "51.75552050", "-1.22615970", "edu", ""], ["Extracting Moving People from Internet Videos", "", "Princeton University", "Princeton University", "Lot 9, University Place, Princeton Township, Mercer County, New Jersey, 08540, USA", "40.34829285", "-74.66308325", "edu", ""], ["Surveillance Video Analysis with External Knowledge and Internal Constraints", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["HUMAN POSE ESTIMATION FROM A SINGLE VIEW POINT by", "", "University of Southern California", "University of Southern California", "University of Southern California, Watt Way, Saint James Park, LA, Los Angeles County, California, 90089, USA", "34.02241490", "-118.28634407", "edu", ""], ["Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["A Heuristic Deformable Pedestrian Detection Method", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Multi-Scale Human Pose Tracking in 2D Monocular Images", "", "Curtin University", "Curtin University", "Curtin University, Brand Drive, Waterford, Perth, Western Australia, 6102, Australia", "-32.00686365", "115.89691775", "edu", ""], ["Finding People Using Scale, Rotation and Articulation Invariant Matching", "", "Boston College", "Boston College", "Boston College, 140, Commonwealth Avenue, Chestnut Hill, Newton, Middlesex County, Massachusetts, 02467, USA", "42.33544810", "-71.16813864", "edu", ""], ["Training Object Class Detectors with Click Supervision", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Video retrieval by mimicking poses", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Shape-based pedestrian parsing", "", "Beijing Jiaotong University", "Beijing Jiaotong University", "\u5317\u4eac\u4ea4\u901a\u5927\u5b66, \u94f6\u674f\u5927\u9053, \u7a3b\u9999\u56ed\u5357\u793e\u533a, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100044, \u4e2d\u56fd", "39.94976005", "116.33629046", "edu", ""], ["ChaLearn Looking at People Challenge 2014: Dataset and Results", "", "Microsoft Research Asia", "Microsoft Live Labs Research, China", "Microsoft Research Asia", "35.86166000", "104.19539700", "company", ""], ["2D and 3D Pose Recovery from a Single Uncalibrated Video - A View and Activity Independent Framework", "", "Kingston University", "Kingston University", "Kingston University, Kingston Hill, Kingston Vale, Kingston-upon-Thames, London, Greater London, England, KT2 7TF, UK", "51.42930860", "-0.26840440", "edu", ""], ["A Unified Contour-Pixel Model for Figure-Ground Segmentation", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Efficient and Exact MAP-MRF Inference using Branch and Bound", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["Gamesourcing to acquire labeled human pose estimation data", "", "University of North Carolina at Charlotte", "University of North Carolina at Charlotte", "Lot 20, Poplar Terrace Drive, Charlotte, Mecklenburg County, North Carolina, 28223, USA", "35.31034410", "-80.73261617", "edu", ""], ["A Hierarchical Model of Shape and Appearance for Human Action Classification", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["R-CNNs for Pose Estimation and Action Detection", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["Chained Predictions Using Convolutional Neural Networks", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["It's All About the Data", "Computer Science, U.Illinois at Urbana Champaign, Urbana, United States", "University of Illinois, Urbana Champaign", "Computer Science, U.Illinois at Urbana Champaign, Urbana, United States", "Champaign, IL, USA", "40.10195230", "-88.22716150", "edu", ""], ["Localizing Objects While Learning Their Appearance", "", "ETH Zurich", "Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland", "Sternwartstrasse 7, 8092 Z\u00fcrich, Switzerland", "47.37723980", "8.55216180", "edu", ""], ["Fast Energy Minimization Using Learned State Filters", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Integration of Local Image Cues for Probabilistic 2D Pose Recovery", "", "Kingston University", "Kingston University", "Kingston University, Kingston Hill, Kingston Vale, Kingston-upon-Thames, London, Greater London, England, KT2 7TF, UK", "51.42930860", "-0.26840440", "edu", ""], ["Beyond KernelBoost", "", "ETH Z\u00fcrich", "ETH Z\u00fcrich", "ETH Z\u00fcrich, 101, R\u00e4mistrasse, Hochschulen, Altstadt, Z\u00fcrich, Bezirk Z\u00fcrich, Z\u00fcrich, 8092, Schweiz/Suisse/Svizzera/Svizra", "47.37645340", "8.54770931", "edu", ""], ["Max Margin AND/OR Graph learning for parsing the human body", "", "University of California, Los Angeles", "University of California, Los Angeles", "200 UCLA, Medical Plaza Driveway Suite 540, Los Angeles, CA 90095, USA", "34.06877880", "-118.44500940", "edu", ""], ["Understanding Human Motion : Recognition and Retrieval of Human Activities", "", "Bilkent University", "Bilkent University", "Bilkent \u00dcniversitesi, 3. Cadde, \u00dcniversiteler Mahallesi, Ankara, \u00c7ankaya, Ankara, \u0130\u00e7 Anadolu B\u00f6lgesi, 06800, T\u00fcrkiye", "39.87204890", "32.75395155", "edu", ""], ["Improved Human Parsing with a Full Relational Model", "", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["Human Pose Estimation for Multiple Frames", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Human Pose Estimation : Extension and Application", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology", "International Institute of Information Technology, Hyderabad, Campus Road, Ward 105 Gachibowli, Greater Hyderabad Municipal Corporation West Zone, Hyderabad, Rangareddy District, Telangana, 500032, India", "17.44549570", "78.34854698", "edu", ""], ["Automatic learning of British Sign Language from signed TV broadcasts", "", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["Spatio-Temporal GrabCut human segmentation for face and pose recovery", "University of Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain", "University of Barcelona", "University of Barcelona", "Universitat de Barcelona, Carrer de la Diputaci\u00f3, l'Antiga Esquerra de l'Eixample, Eixample, Barcelona, BCN, CAT, 08013, Espa\u00f1a", "41.38689130", "2.16352385", "edu", ""], ["Video Monitoring and Analysis of Human Behavior for Diagnosis of Obstructive Sleep Apnoea", "", "University of Lincoln", "University of Lincoln", "University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK", "53.22853665", "-0.54873472", "edu", ""], ["3D Human Pose Estimation from Monocular Image Sequences", "", "Aalborg University", "Aalborg University", "AAU, Pontoppidanstr\u00e6de, S\u00f8nder Tranders, Aalborg, Aalborg Kommune, Region Nordjylland, 9220, Danmark", "57.01590275", "9.97532827", "edu", ""], ["Unsupervised Video Adaptation for Parsing Human Motion", "", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Using Models of Objects with Deformable Parts for Joint Categorization and Segmentation of Objects", "", "University of California, Berkeley", "University of California, Berkeley", "Berkeley Art Museum and Pacific Film Archive, Bancroft Way, Southside, Berkeley, Alameda County, California, 94720-1076, USA", "37.86871260", "-122.25586815", "edu", ""], ["Towards Understanding Articulated Objects", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Appearance Descriptors for Person Re-identification: a Comprehensive Review", "", "University of Cagliari", "University of Cagliari, Italy", "Via Universit\u00e0, 40, 09124 Cagliari CA, Italy", "39.21736570", "9.11492180", "edu", ""], ["Human Pose Estimation from Monocular Images: A Comprehensive Survey", "", "Shandong University", "Shandong University", "\u5c71\u4e1c\u5927\u5b66, \u6cf0\u5b89\u8857, \u9ccc\u5c71\u536b\u8857\u9053, \u5373\u58a8\u533a, \u9752\u5c9b\u5e02, \u5c71\u4e1c\u7701, 266200, \u4e2d\u56fd", "36.36934730", "120.67381800", "edu", ""], ["Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice", "York University, Toronto, ON, Canada", "York University", "York University", "York University, Keele Campus, Campus Walk, North York, Toronto, Ontario, M3J 2S5, Canada", "43.77439110", "-79.50481085", "edu", ""], ["Metadata of the chapter that will be visualized in SpringerLink", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", ""], ["Holistic Shape-Based Object Recognition Using Bottom-Up Image Structures", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Static Pose Estimation from Depth Images using Random Regression Forests and Hough Voting", "", "University of Surrey", "University of Surrey", "University of Surrey, Spine Road, Guildford Park, Guildford, Surrey, South East, England, GU2 7XH, UK", "51.24303255", "-0.59001382", "edu", ""], ["Utility data annotation with Amazon Mechanical Turk", "University of Illinois at Urbana-Champaign, 201 N Goodwin, 61820, USA", "University of Illinois, Urbana-Champaign", "University of Illinois, Urbana-Champaign", "B-3, South Mathews Avenue, Urbana, Champaign County, Illinois, 61801, USA", "40.11116745", "-88.22587665", "edu", ""], ["2D Human Pose Estimation in TV Shows", "", "University of Oxford", "University of Oxford", "Radcliffe Camera, Radcliffe Square, Grandpont, Oxford, Oxon, South East, England, OX1 4AJ, UK", "51.75345380", "-1.25400997", "edu", ""], ["An efficient branch-and-bound algorithm for optimal human pose estimation", "", "University of Michigan", "University of Michigan", "University of Michigan, 500, Hayward Street, Ann Arbor, Washtenaw County, Michigan, 48109, USA", "42.29421420", "-83.71003894", "edu", ""], ["From Pictorial Structures to deformable structures", "", "Brown University", "Brown University", "Brown University, Waterman Street, College Hill, Providence, Bristol, Rhode Island, 02912, USA", "41.82686820", "-71.40123146", "edu", ""], ["Articulated Pose Estimation Using Discriminative Armlet Classifiers", "", "Facebook", "Facebook", "250 Bryant St, Mountain View, CA 94041, USA", "37.39367170", "-122.08072620", "company", "Facebook, Mountain View, CA"], ["Parsing human motion with stretchable models", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Annotation and taxonomy of gestures in lecture videos", "", "Columbia University", "Columbia University", "Columbia University Medical Center, 630, West 168th Street, Washington Heights, Manhattan, Manhattan Community Board 12, New York County, NYC, New York, 10031, USA", "40.84198360", "-73.94368971", "edu", ""], ["Fine-grained classification of pedestrians in video: Benchmark and state of the art", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Training object class detectors with click supervision Dim", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Mirror, mirror on the wall, tell me, is the error small?", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", ""], ["PixelNet: Representation of the pixels, by the pixels, and for the pixels", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["3-D\u20132-D spatiotemporal registration for sports motion analysis", "", "National University of Singapore", "National University of Singapore", "NUS, Former 1936 British Outpost, Nepal Hill, Clementi, Southwest, 117542, Singapore", "1.29620180", "103.77689944", "edu", ""], ["Part-based pose estimation with local and non-local contextual information", "", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Human Pose Estimation in Videos", "", "University of Central Florida", "University of Central Florida", "University of Central Florida, Libra Drive, University Park, Orange County, Florida, 32816, USA", "28.59899755", "-81.19712501", "edu", ""], ["Appearance Sharing for Collective Human Pose Estimation", "", "University of Edinburgh", "University of Edinburgh", "New College, New College Courtyard, The Mound, Old Town, Edinburgh, City of Edinburgh, Scotland, EH1 2LX, UK", "55.94951105", "-3.19534913", "edu", ""], ["Parsing clothing in fashion photographs", "", "Stony Brook University", "Stony Brook University", "Stony Brook University, 100, Nicolls Road, Stony Brook, Suffolk County, New York, 11794, USA", "40.91531960", "-73.12706260", "edu", ""], ["Context Aware Active Learning of Activity Recognition Models", "", "University of California, Riverside", "University of California, Riverside", "University of California, Riverside, Linden Street, Riverside, Riverside County, California, 92521, USA", "33.98071305", "-117.33261035", "edu", ""], ["Learning Visual Symbols for Parsing Human Poses in Images", "", "Nanjing University", "Nanjing University", "NJU, \u4e09\u6c5f\u8def, \u9f13\u697c\u533a, \u5357\u4eac\u5e02, \u6c5f\u82cf\u7701, 210093, \u4e2d\u56fd", "32.05659570", "118.77408833", "edu", ""], ["Cascaded Models for Articulated Pose Estimation", "", "University of Pennsylvania", "University of Pennsylvania", "Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA", "39.94923440", "-75.19198985", "edu", ""], ["Patch-based models for visual object classes", "University College London", "University College London", "University College London", "UCL Institute of Education, 20, Bedford Way, Holborn, Bloomsbury, London Borough of Camden, London, Greater London, England, WC1H 0AL, UK", "51.52316070", "-0.12820370", "edu", ""], ["Machine Perception for Occupational Therapy", "", "University of Pittsburgh", "University of Pittsburgh", "University of Pittsburgh, Sutherland Drive, West Oakland, PGH, Allegheny County, Pennsylvania, 15240, USA", "40.44415295", "-79.96243993", "edu", ""], ["A Layered Approach for Robust Spatial Virtual Human Pose Reconstruction Using a Still Image", "", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Human Context: Modeling Human-Human Interactions for Monocular 3D Pose Estimation", "", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["Fast Multi-aspect 2D Human Detection", "", "Boston University", "Boston University", "BU, Bay State Road, Fenway, Boston, Suffolk County, Massachusetts, 02215, USA", "42.35042530", "-71.10056114", "edu", ""]]}
\ No newline at end of file
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new file mode 100644
index 00000000..e4ff990a
--- /dev/null
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\ No newline at end of file
diff --git a/site/datasets/citations/8355d095d3534ef511a9af68a3b2893339e3f96b.json b/site/datasets/citations/8355d095d3534ef511a9af68a3b2893339e3f96b.json
new file mode 100644
index 00000000..699fc928
--- /dev/null
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@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/site/datasets/citations/98bb029afe2a1239c3fdab517323066f0957b81b.json b/site/datasets/citations/98bb029afe2a1239c3fdab517323066f0957b81b.json
new file mode 100644
index 00000000..671a6707
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diff --git a/site/datasets/citations/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.json b/site/datasets/citations/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.json
new file mode 100644
index 00000000..cfe3f6ef
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\ No newline at end of file
diff --git a/site/datasets/citations/b1f4423c227fa37b9680787be38857069247a307.json b/site/datasets/citations/b1f4423c227fa37b9680787be38857069247a307.json
new file mode 100644
index 00000000..d9262550
--- /dev/null
+++ b/site/datasets/citations/b1f4423c227fa37b9680787be38857069247a307.json
@@ -0,0 +1 @@
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Alberta, Edmonton, Alberta, T6G, Canada", "53.52385720", "-113.52282665", "edu", ""], ["UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking", "University at Albany, SUNY", "Hanyang University", "Hanyang University", "\ud55c\uc591\ub300, 206, \uc655\uc2ed\ub9ac\ub85c, \uc0ac\uadfc\ub3d9, \uc131\ub3d9\uad6c, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, 04763, \ub300\ud55c\ubbfc\uad6d", "37.55572710", "127.04366420", "edu", ""], ["An MRF-Poselets Model for Detecting Highly Articulated Humans", "", "Singapore University of Technology and Design", "Singapore University of Technology and Design", "Singapore University of Technology and Design, Simpang Bedok, Changi Business Park, Southeast, 486041, Singapore", "1.34021600", "103.96508900", "edu", ""], ["Convolutional Channel Features : Tailoring CNN to Diverse Tasks", "Institute of Automation, Chinese Academy of Sciences, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", 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"\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["DeepSetNet: Predicting Sets with Deep Neural Networks", "", "University of Adelaide", "University of Adelaide", "University of Adelaide, North Terrace, Adelaide, 5000, City of Adelaide, South Australia, 5000, Australia", "-34.91892260", "138.60423668", "edu", ""], ["Depth Information Guided Crowd Counting for Complex Crowd Scenes", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["How do you develop a face detector for the unconstrained environment?", "The University of Queensland, Brisbane, Australia", "University of Queensland", "University of Queensland", "University of Queensland, University Drive, Hill End, St Lucia, Brisbane, QLD, 4072, Australia", "-27.49741805", "153.01316956", "edu", ""], ["Deep Learning of Scene-Specific Classifier for Pedestrian Detection", "", "Shenzhen Institutes of Advanced Technology", "Shenzhen Institutes of Advanced Technology", "\u4e2d\u56fd\u79d1\u5b66\u9662\u6df1\u5733\u5148\u8fdb\u6280\u672f\u7814\u7a76\u9662, 1068, \u79d1\u7814\u8def, \u6df1\u5733\u5927\u5b66\u57ce, \u4e09\u5751\u6751, \u5357\u5c71\u533a, \u6df1\u5733\u5e02, \u5e7f\u4e1c\u7701, 518000, \u4e2d\u56fd", "22.59805605", "113.98533784", "edu", ""], ["The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking", "", "University of Texas at San Antonio", "University of Texas at San Antonio", "UTSA, Paseo Principal, San Antonio, Bexar County, Texas, 78249-1620, USA", "29.58333105", "-98.61944505", "edu", ""], ["Illuminating Pedestrians via Simultaneous Detection and Segmentation", "", "Michigan State University", "Michigan State University", "Michigan State University, Farm Lane, East Lansing, Ingham County, Michigan, 48824, USA", "42.71856800", "-84.47791571", "edu", ""], ["Hybrid Human-Machine Vision Systems: Image Annotation using Crowds, Experts and Machines", "", "California Institute of Technology", "California Institute of Technology", "California Institute of Technology, San Pasqual Walk, Madison Heights, Pasadena, Los Angeles County, California, 91126, USA", "34.13710185", "-118.12527487", "edu", ""], ["Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly", "", "University of Texas at Austin", "University of Texas at Austin", "University of Texas at Austin, 1, East 23rd Street, The Drag, Austin, Travis County, Texas, 78712, USA", "30.28415100", "-97.73195598", "edu", ""], ["Tracking articulated human movements witha component based approach to boosted multiple instance learning", "", "Purdue University", "Purdue University", "Purdue University, West Stadium Avenue, West Lafayette, Tippecanoe County, Indiana, 47907, USA", "40.43197220", "-86.92389368", "edu", ""], ["Normalized Autobinomial Markov Channels For Pedestrian Detection", "", "University Politehnica of Bucharest", "University Politehnica of Bucharest", "Universitatea Politehnica din Bucure\u0219ti, Novum Invest, Bucure\u0219ti, Militari, Sector 6, Municipiul Bucure\u0219ti, 060042, Rom\u00e2nia", "44.43918115", "26.05044565", "edu", ""], ["Incremental Domain Adaptation of Deformable Part-based Models", "", "Universitat Aut\u00f2noma de Barcelona", "Universitat Aut\u00f2noma de Barcelona", "Centre de Visi\u00f3 per Computador (CVC), Carrer de l'Albareda, Serraperera, UAB, Cerdanyola del Vall\u00e8s, Vall\u00e8s Occidental, BCN, CAT, 08214, Espa\u00f1a", "41.50078110", "2.11143663", "edu", ""], ["SLTP: A Fast Descriptor for People Detection in Depth Images", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["CityPersons: A Diverse Dataset for Pedestrian Detection", "", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["Online Motion Agreement Tracking", "", "Boston University", "Boston University", "BU, Bay State Road, Fenway, Boston, Suffolk County, Massachusetts, 02215, USA", "42.35042530", "-71.10056114", "edu", ""], ["Online Motion Agreement Tracking", "", "Boston University", "Boston University", "BU, Bay State Road, Fenway, Boston, Suffolk County, Massachusetts, 02215, USA", "42.35042530", "-71.10056114", "edu", ""], ["Robust Multi-resolution Pedestrian Detection in Traffic Scenes", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition", "", "Beijing University of Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "\u5317\u4eac\u90ae\u7535\u5927\u5b66, \u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps", "", "East China Normal University", "East China Normal University", "\u534e\u4e1c\u5e08\u8303\u5927\u5b66, 3663, \u4e2d\u5c71\u5317\u8def, \u66f9\u5bb6\u6e21, \u666e\u9640\u533a, \u666e\u9640\u533a (Putuo), 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["Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors", "", "Seoul National University", "Seoul National University", "\uc11c\uc6b8\ub300\ud559\uad50, \uc11c\ud638\ub3d9\ub85c, \uc11c\ub454\ub3d9, \uad8c\uc120\uad6c, \uc218\uc6d0\uc2dc, \uacbd\uae30, 16614, \ub300\ud55c\ubbfc\uad6d", "37.26728000", "126.98411510", "edu", ""], ["Facial expression recognition under a wide range of head poses", "", "University of Trento", "University of Trento", "University of Trento, Via Giuseppe Verdi, Piedicastello, Trento, Territorio Val d'Adige, TN, TAA, 38122, Italia", "46.06588360", "11.11598940", "edu", ""], ["Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features", "", "Simon Fraser University", "Simon Fraser University", "SFU Burnaby, South Campus Road, Barnet, Burnaby, Metro Vancouver, British Columbia, V5A 4X6, Canada", "49.27674540", "-122.91777375", "edu", ""], ["Object Detection: Current and Future Directions", "instance has been detected (e.g., a face), it is be possible to obtain further information, including: (i", "University of Groningen", "University of Groningen", "Academiegebouw, Professorgang, Binnenstad, Groningen, Nederland, 9712EA, Nederland", "53.21967825", "6.56251482", "edu", ""], ["DETRAC: A New Benchmark and Protocol for Multi-Object Tracking", "", "Hanyang University", "Hanyang University", "\ud55c\uc591\ub300, 206, \uc655\uc2ed\ub9ac\ub85c, \uc0ac\uadfc\ub3d9, \uc131\ub3d9\uad6c, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, 04763, \ub300\ud55c\ubbfc\uad6d", "37.55572710", "127.04366420", "edu", ""], ["Face Classification: A Specialized Benchmark Study", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Chinese Academy of Sciences, UCAS, Yuquanlu, \u7389\u6cc9\u8def, \u7530\u6751, \u6d77\u6dc0\u533a, 100049, \u4e2d\u56fd", "39.90828040", "116.24585270", "edu", ""], ["A New Urban Objects Detection Framework Using Weakly Annotated Sets", "University of S ao Paulo - USP, S ao Paulo - Brazil", "New York University", "New York University", "NYU, West 4th Street, NoHo Historic District, NoHo, Manhattan, Manhattan Community Board 2, New York County, NYC, New York, 10012, USA", "40.72925325", "-73.99625394", "edu", ""], ["DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation", "", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University Silicon Valley, South Akron Road, ARC, Santa Clara County, California, 94035-0016, USA", "37.41021930", "-122.05965487", "edu", ""], ["Occluded Pedestrian Detection Through Guided Attention in CNNs", "", "Max Planck Institute for Informatics", "Max Planck Institute for Informatics", "MPII, E1 4, Campus, Universit\u00e4t, Sankt Johann, Bezirk Mitte, Saarbr\u00fccken, Regionalverband Saarbr\u00fccken, Saarland, 66123, Deutschland", "49.25795660", "7.04577417", "edu", ""], ["Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database", "National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8560, Japan", "Keio University", "Keio University", "\u7db1\u5cf6\u5e02\u6c11\u306e\u68ee, \u3051\u3064\u308f\u308a\u5742, \u6e2f\u5317\u533a, \u6a2a\u6d5c\u5e02, \u795e\u5948\u5ddd\u770c, \u95a2\u6771\u5730\u65b9, 223-0053, \u65e5\u672c", "35.54169690", "139.63471840", "edu", ""], ["Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection", "", "University of Maryland", "University of Maryland", "The Grand Garage, 5, North Paca Street, Seton Hill, Baltimore, Maryland, 21201, USA", "39.28996850", "-76.62196103", "edu", ""], ["The Fastest Deformable Part Model for Object Detection", "Institute of Automation, Chinese Academy of Sciences, China", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, 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Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery", "", "Beihang University", "Beihang University", "\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66, 37, \u5b66\u9662\u8def, \u4e94\u9053\u53e3, \u540e\u516b\u5bb6, \u6d77\u6dc0\u533a, 100083, \u4e2d\u56fd", "39.98083330", "116.34101249", "edu", ""], ["Scale Aggregation Network for Accurate and Efficient Crowd Counting", "", "Beijing University of Posts and Telecommunications", "Beijing University of Posts and Telecommunications", "\u5317\u4eac\u90ae\u7535\u5927\u5b66, \u897f\u571f\u57ce\u8def, \u6d77\u6dc0\u533a, \u5317\u4eac\u5e02, 100082, \u4e2d\u56fd", "39.96014880", "116.35193921", "edu", ""], ["Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking", "University of Adelaide, Australia", "University of Adelaide", "University of Adelaide", "University of Adelaide, North Terrace, Adelaide, 5000, City of Adelaide, South Australia, 5000, Australia", "-34.91892260", "138.60423668", "edu", ""], ["UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", ""], ["Joint Deep Learning for Pedestrian Detection", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", ""], ["TabletGaze : Dataset and Algorithm for Unconstrained Appearance-based Gaze Estimation in Mobile Tablets", "", "Rice University", "Rice University", "Rice University, Stockton Drive, Houston, Harris County, Texas, 77005-1890, USA", "29.71679145", "-95.40478113", "edu", ""], ["SPID: Surveillance Pedestrian Image Dataset and Performance Evaluation for Pedestrian Detection", "", "Shanghai Jiao Tong University", "Shanghai Jiao Tong University", "\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\uff08\u5f90\u6c47\u6821\u533a\uff09, \u6dee\u6d77\u897f\u8def, \u756a\u79ba\u5c0f\u533a, \u5e73\u9634\u6865, \u5f90\u6c47\u533a, \u4e0a\u6d77\u5e02, 200052, \u4e2d\u56fd", "31.20081505", "121.42840681", "edu", ""]]}
\ No newline at end of file
diff --git a/site/datasets/citations/fcc6fe6007c322641796cb8792718641856a22a7.json b/site/datasets/citations/fcc6fe6007c322641796cb8792718641856a22a7.json
index adb35dd1..8bba3b62 100644
--- a/site/datasets/citations/fcc6fe6007c322641796cb8792718641856a22a7.json
+++ b/site/datasets/citations/fcc6fe6007c322641796cb8792718641856a22a7.json
@@ -1 +1 @@
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