diff options
| author | Jules Laplace <julescarbon@gmail.com> | 2018-12-16 15:02:59 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2018-12-16 15:02:59 +0100 |
| commit | 110f3a34f1f36d0ea999d4aa34bbe66d5f2a01da (patch) | |
| tree | f21fbeccb6a7e8d3af5d5c537ed5931ecbd62d7e /scraper | |
| parent | 2cb31d4999649a22a0ac659a59a0aa0a0f7a241e (diff) | |
skip empty, pull citations again
Diffstat (limited to 'scraper')
| -rw-r--r-- | scraper/README.md | 4 | ||||
| -rw-r--r-- | scraper/pdf_dump_first_page.sh | 8 | ||||
| -rw-r--r-- | scraper/reports/report_coverage.html | 2 | ||||
| -rw-r--r-- | scraper/reports/report_index.html | 2 | ||||
| -rw-r--r-- | scraper/s2-citation-report.py | 8 | ||||
| -rw-r--r-- | scraper/s2-geocode-spreadsheet.py | 2 | ||||
| -rw-r--r-- | scraper/util.py | 16 |
7 files changed, 28 insertions, 14 deletions
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>
\ No newline at end of file +<!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>
\ No newline at end of file diff --git a/scraper/reports/report_index.html b/scraper/reports/report_index.html index a84a6295..ea6b5111 100644 --- a/scraper/reports/report_index.html +++ b/scraper/reports/report_index.html @@ -1 +1 @@ -<!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>
\ No newline at end of file +<!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 |