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diff --git a/scraper/reports/report_coverage.html b/scraper/reports/report_coverage.html index a078c87f..9243895f 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="https://arxiv.org/pdf/1212.0402.pdf">[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>257</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>306</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>303</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>303</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>303</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>303</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>303</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>455</td><td>439</td><td>56</td><td>589</td><td>242</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>423</td><td>576</td><td>77</td><td>538</td><td>331</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>423</td><td>576</td><td>77</td><td>538</td><td>331</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>395</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>392</td><td>607</td><td>74</td><td>730</td><td>211</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="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>39%</td><td>999</td><td>392</td><td>607</td><td>71</td><td>621</td><td>341</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>381</td><td>618</td><td>74</td><td>556</td><td>267</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>94</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>106</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>45%</td><td>742</td><td>332</td><td>410</td><td>60</td><td>412</td><td>244</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>76</td><td>699</td><td>247</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>309</td><td>539</td><td>55</td><td>413</td><td>288</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>30%</td><td>999</td><td>304</td><td>695</td><td>94</td><td>671</td><td>246</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>157</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>111</td><td>573</td><td>297</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="https://arxiv.org/pdf/1604.01685.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>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>135</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="https://arxiv.org/pdf/1604.01685.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>33%</td><td>771</td><td>252</td><td>519</td><td>54</td><td>622</td><td>135</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>166</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html">The FERET Promising Research database and evaluation procedure for face - recognition algorithms</a></td><td><a href="http://pdfs.semanticscholar.org/dc8b/25e35a3acb812beb499844734081722319b4.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>24%</td><td>999</td><td>237</td><td>762</td><td>105</td><td>584</td><td>300</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>237</td><td>762</td><td>64</td><td>483</td><td>359</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>24%</td><td>999</td><td>235</td><td>764</td><td>103</td><td>539</td><td>315</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>232</td><td>204</td><td>32</td><td>284</td><td>141</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>67</td><td>487</td><td>162</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>22%</td><td>999</td><td>218</td><td>781</td><td>91</td><td>585</td><td>240</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>22%</td><td>999</td><td>218</td><td>781</td><td>91</td><td>585</td><td>240</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>22%</td><td>999</td><td>218</td><td>781</td><td>91</td><td>585</td><td>240</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>358</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>551</td><td>324</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>166</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>166</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="http://doi.org/10.1038/nn1421">[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>61</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>19%</td><td>999</td><td>187</td><td>812</td><td>70</td><td>530</td><td>344</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5975165">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>19%</td><td>999</td><td>187</td><td>812</td><td>70</td><td>530</td><td>344</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>160</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909866">[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>48</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>115</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298">[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>103</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>203</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>73</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>73</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>73</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>112</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>130</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>84</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>48</td><td>284</td><td>207</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>47</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>126</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="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf">[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>79</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>29</td><td>212</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>13</td><td>199</td><td>67</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>13</td><td>199</td><td>67</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>13</td><td>199</td><td>67</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>35</td><td>243</td><td>98</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>35</td><td>243</td><td>98</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>146</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>146</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>146</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>42%</td><td>269</td><td>114</td><td>155</td><td>29</td><td>212</td><td>50</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>42%</td><td>269</td><td>114</td><td>155</td><td>29</td><td>212</td><td>50</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>57</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>55%</td><td>185</td><td>101</td><td>84</td><td>14</td><td>117</td><td>57</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>55%</td><td>185</td><td>101</td><td>84</td><td>14</td><td>117</td><td>57</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>55%</td><td>185</td><td>101</td><td>84</td><td>14</td><td>117</td><td>57</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>50</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995711">[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>48</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>137</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>27</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>49%</td><td>167</td><td>82</td><td>85</td><td>15</td><td>131</td><td>33</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>91</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="https://arxiv.org/pdf/1511.06523.pdf">[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>39</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>148</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>34</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>148</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>34</td><td>201</td><td>78</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>34</td><td>201</td><td>78</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>56</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>60</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>64</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995318">[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>10</td><td>117</td><td>50</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>-35.27769990</td><td>149.11852700</td><td>38%</td><td>182</td><td>69</td><td>113</td><td>8</td><td>83</td><td>87</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200254">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>-35.27769990</td><td>149.11852700</td><td>38%</td><td>182</td><td>69</td><td>113</td><td>8</td><td>83</td><td>87</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>21%</td><td>330</td><td>68</td><td>262</td><td>27</td><td>185</td><td>117</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>21%</td><td>330</td><td>68</td><td>262</td><td>27</td><td>185</td><td>117</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0633.pdf">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td>21%</td><td>330</td><td>68</td><td>262</td><td>27</td><td>185</td><td>117</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>101</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>22</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>101</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>37</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>19</td><td>144</td><td>136</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>26</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771462">[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>55</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>85</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>114</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>48</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>26</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="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>27%</td><td>210</td><td>56</td><td>153</td><td>10</td><td>114</td><td>82</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="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>27%</td><td>210</td><td>56</td><td>153</td><td>10</td><td>114</td><td>82</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="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td>27%</td><td>210</td><td>56</td><td>153</td><td>10</td><td>114</td><td>82</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553788">[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>60</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>17</td><td>99</td><td>35</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>39</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>123</td><td>51</td><td>72</td><td>3</td><td>70</td><td>44</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>49</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>22</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>73</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>42</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7406390">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td>39%</td><td>120</td><td>47</td><td>73</td><td>6</td><td>71</td><td>39</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>60</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>60</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="https://arxiv.org/pdf/1408.3967.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>36%</td><td>110</td><td>40</td><td>70</td><td>12</td><td>67</td><td>39</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="https://arxiv.org/pdf/1408.3967.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>36%</td><td>110</td><td>40</td><td>70</td><td>12</td><td>67</td><td>39</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="https://arxiv.org/pdf/1408.3967.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>36%</td><td>110</td><td>40</td><td>70</td><td>12</td><td>67</td><td>39</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="https://arxiv.org/pdf/1408.3967.pdf">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td>36%</td><td>110</td><td>40</td><td>70</td><td>12</td><td>67</td><td>39</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>50</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>25</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467308">[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>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>6</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>5</td><td>58</td><td>12</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>12</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780969">[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>16</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>29%</td><td>112</td><td>32</td><td>80</td><td>11</td><td>76</td><td>24</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>19</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>17</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994">[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>23</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>7</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994">[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>23</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6612994">[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>23</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>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>20</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>28%</td><td>99</td><td>28</td><td>71</td><td>9</td><td>63</td><td>26</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>31%</td><td>90</td><td>28</td><td>62</td><td>5</td><td>57</td><td>24</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>31%</td><td>90</td><td>28</td><td>62</td><td>5</td><td>57</td><td>24</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813376">[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>29</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>54</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>31</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html">FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results</a></td><td><a href="http://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td>32%</td><td>82</td><td>26</td><td>56</td><td>5</td><td>61</td><td>13</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>14</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788">[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>8</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>16</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981788">[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>8</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>20</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>25%</td><td>96</td><td>24</td><td>72</td><td>2</td><td>31</td><td>51</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>25%</td><td>96</td><td>24</td><td>72</td><td>2</td><td>31</td><td>51</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>25%</td><td>96</td><td>24</td><td>72</td><td>2</td><td>31</td><td>51</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>24</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>14</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>23</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>41</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="https://arxiv.org/pdf/1710.08092.pdf">[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>6</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>32</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="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf">[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>8</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>11</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237796">[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>7</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>36</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>4</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="https://pdfs.semanticscholar.org/0d43/3b9435b874a1eea6d7999e86986c910fa285.pdf">[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>6</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>16</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>19%</td><td>83</td><td>16</td><td>67</td><td>5</td><td>50</td><td>24</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>19%</td><td>83</td><td>16</td><td>67</td><td>5</td><td>50</td><td>24</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>6</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>5</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="https://arxiv.org/pdf/1801.03150.pdf">[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>1</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="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf">[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>11</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="https://arxiv.org/pdf/1705.00393.pdf">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td>37%</td><td>38</td><td>14</td><td>24</td><td>2</td><td>29</td><td>8</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>5</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>15</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>9</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866883">[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>39</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>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>15</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="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf">[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>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>42%</td><td>26</td><td>11</td><td>15</td><td>1</td><td>17</td><td>9</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>42%</td><td>26</td><td>11</td><td>15</td><td>1</td><td>17</td><td>9</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>24%</td><td>45</td><td>11</td><td>34</td><td>7</td><td>34</td><td>10</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>3</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>13</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>19</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>12</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>13</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>4</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014984">[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>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>5</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>5</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="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf">[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>24</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>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>20</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>3</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401928">[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>7</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><a href="https://arxiv.org/pdf/1705.07426.pdf">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td>32%</td><td>25</td><td>8</td><td>17</td><td>3</td><td>17</td><td>6</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>5</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>11</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>1</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>13</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="https://arxiv.org/pdf/1711.08565.pdf">[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>3</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>29</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>7</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>5</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html">Object recognition using segmentation for feature detection</a></td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td></td><td></td><td></td><td></td><td>21%</td><td>29</td><td>6</td><td>23</td><td>1</td><td>21</td><td>7</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html">Object recognition using segmentation for feature detection</a></td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td></td><td></td><td></td><td></td><td>21%</td><td>29</td><td>6</td><td>23</td><td>1</td><td>21</td><td>7</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html">Object recognition using segmentation for feature detection</a></td><td><a href="http://www.emt.tugraz.at/~tracking/Publications/fussenegger2004b.pdf">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td></td><td></td><td></td><td></td><td>21%</td><td>29</td><td>6</td><td>23</td><td>1</td><td>21</td><td>7</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>4</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="https://arxiv.org/pdf/1511.02459.pdf">[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>8</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>2</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>2</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>6</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>13</td><td>0</td><td>11</td><td>7</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>13</td><td>0</td><td>11</td><td>7</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>14</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="https://arxiv.org/pdf/1605.09653.pdf">[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>4</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>10</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>16</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>4</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>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="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf">[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>11</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="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf">[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>1</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="https://arxiv.org/pdf/1701.02664.pdf">[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>4</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7789672">[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>2</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411217">[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>35ba4ebfd017a56b51e967105af9ae273c9b0178</td><td>kitti</td><td>KITTI</td><td><a href="papers/35ba4ebfd017a56b51e967105af9ae273c9b0178.html">The Role of Machine Vision for Intelligent Vehicles</a></td><td><a href="http://www.path.berkeley.edu/sites/default/files/my_folder_76/Pub_03.2016_Role.pdf">[pdf]</a></td><td>IEEE Transactions on Intelligent Vehicles</td><td></td><td></td><td></td><td></td><td>17%</td><td>18</td><td>3</td><td>15</td><td>0</td><td>6</td><td>9</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>1</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>4</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>7</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8337841">[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>1</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423747">[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>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>3</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>3</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>4</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>1</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>4</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>4</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>6</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>3</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>1</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="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf">[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>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="https://arxiv.org/pdf/1708.09687.pdf">[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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686">[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>4</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td>14%</td><td>7</td><td>1</td><td>6</td><td>0</td><td>7</td><td>0</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>1</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="https://arxiv.org/pdf/1703.06283.pdf">[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>1</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>5</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="https://arxiv.org/pdf/1712.01443.pdf">[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>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="http://dl.acm.org/citation.cfm?id=3232665">[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="https://arxiv.org/pdf/1805.07193.pdf">[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>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>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</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>0</td><td>0</td><td>0</td><td>0</td><td>0</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="https://pdfs.semanticscholar.org/b6b1/b0632eb9d4ab1427278f5e5c46f97753c73d.pdf">[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>1</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>2</td><td>0</td><td>2</td><td>0</td><td>0</td><td>2</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>2</td><td>0</td><td>2</td><td>0</td><td>0</td><td>2</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>4</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>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>1</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>2</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>8</td><td>0</td><td>3</td><td>4</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>8</td><td>0</td><td>3</td><td>4</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="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8014998">[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>5</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>2</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="https://arxiv.org/pdf/1804.09691.pdf">[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>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</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>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="https://arxiv.org/pdf/1804.04326.pdf">[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>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf">[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="https://arxiv.org/pdf/1804.10275.pdf">[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>4</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="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf">[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="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf">[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>1</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>9</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>Country</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>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html" target="_blank">Face detection, pose estimation, and landmark localization in the wild</a></td><td><a href="http://crcv.ucf.edu/courses/CAP6412/Spring2013/papers/zhu-ramanan-face-cvpr12.pdf" target="_blank">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>999</td><td>623</td><td>376</td><td>59</td><td>622</td><td>387</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html" target="_blank">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>999</td><td>614</td><td>385</td><td>73</td><td>716</td><td>283</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html" target="_blank">Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</a></td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>999</td><td>591</td><td>406</td><td>71</td><td>639</td><td>371</td></tr><tr><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td><td>voc</td><td>VOC</td><td><a href="papers/0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a.html" target="_blank">The Pascal Visual Object Classes (VOC) Challenge</a></td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>59%</td><td>999</td><td>587</td><td>412</td><td>35</td><td>611</td><td>414</td></tr><tr><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td><td>a_pascal_yahoo</td><td>aPascal</td><td><a href="papers/2e384f057211426ac5922f1b33d2aa8df5d51f57.html" target="_blank">Describing objects by their attributes</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0468.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>999</td><td>575</td><td>423</td><td>74</td><td>738</td><td>264</td></tr><tr><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td><td>coco</td><td>COCO</td><td><a href="papers/5e0f8c355a37a5a89351c02f174e7a5ddcb98683.html" target="_blank">Microsoft COCO: Common Objects in Context</a></td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>999</td><td>574</td><td>425</td><td>29</td><td>799</td><td>193</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td>edu</td><td>University of Pittsburgh</td><td>United States</td><td>40.44415295</td><td>-79.96243993</td><td>58%</td><td>975</td><td>569</td><td>405</td><td>67</td><td>475</td><td>510</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html" target="_blank">Histograms of oriented gradients for human detection</a></td><td><a href="http://courses.cs.washington.edu/courses/cse576/12sp/notes/CVPR2005_HOG.pdf" target="_blank">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>France</td><td>45.21788600</td><td>5.80736900</td><td>57%</td><td>999</td><td>565</td><td>434</td><td>67</td><td>537</td><td>477</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html" target="_blank">Attribute and simile classifiers for face verification</a></td><td><a href="http://acberg.com/papers/kbbn09iccv.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>894</td><td>557</td><td>337</td><td>56</td><td>604</td><td>300</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html" target="_blank">Deep Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>550</td><td>449</td><td>70</td><td>635</td><td>370</td></tr><tr><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/6d96f946aaabc734af7fe3fc4454cf8547fcd5ed.html" target="_blank">The AR face database</a></td><td><span class="gray">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>999</td><td>541</td><td>458</td><td>51</td><td>459</td><td>573</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html" target="_blank">80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</a></td><td><a href="http://cvcl.mit.edu/SUNSeminar/Torralba_80M_PAMI08.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>999</td><td>540</td><td>458</td><td>94</td><td>685</td><td>327</td></tr><tr><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td><td>kitti</td><td>KITTI</td><td><a href="papers/026e3363b7f76b51cc711886597a44d5f1fd1de2.html" target="_blank">Vision meets robotics: The KITTI dataset</a></td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td>I. J. Robotics Res.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>999</td><td>538</td><td>461</td><td>37</td><td>570</td><td>448</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html" target="_blank">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>999</td><td>532</td><td>467</td><td>75</td><td>572</td><td>439</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html" target="_blank">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="https://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>999</td><td>528</td><td>471</td><td>77</td><td>551</td><td>459</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html" target="_blank">Overview of the face recognition grand challenge</a></td><td><a href="http://ivizlab.sfu.ca/arya/Papers/IEEE/Proceedings/C%20V%20P%20R-%2005/Face%20Recognition%20Grand%20Challenge.pdf" target="_blank">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>NIST</td><td>United States</td><td>39.14004000</td><td>-77.21850600</td><td>51%</td><td>999</td><td>513</td><td>485</td><td>114</td><td>594</td><td>424</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html" target="_blank">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" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>999</td><td>510</td><td>489</td><td>110</td><td>525</td><td>485</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html" target="_blank">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>999</td><td>503</td><td>496</td><td>103</td><td>560</td><td>454</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html" target="_blank">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><a href="http://vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/PAMI12pedestrians.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>49%</td><td>999</td><td>493</td><td>506</td><td>71</td><td>541</td><td>464</td></tr><tr><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/b62628ac06bbac998a3ab825324a41a11bc3a988.html" target="_blank">Xm2vtsdb: the Extended M2vts Database</a></td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>906</td><td>491</td><td>415</td><td>44</td><td>542</td><td>408</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html" target="_blank">Parameterisation of a stochastic model for human face identification</a></td><td><a href="https://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>999</td><td>466</td><td>533</td><td>97</td><td>569</td><td>445</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html" target="_blank">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="https://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>848</td><td>464</td><td>384</td><td>55</td><td>420</td><td>433</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html" target="_blank">The FERET database and evaluation procedure for face-recognition algorithms</a></td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>999</td><td>462</td><td>537</td><td>106</td><td>606</td><td>413</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html" target="_blank">VQA: Visual Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.00468.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>731</td><td>449</td><td>282</td><td>47</td><td>629</td><td>96</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html" target="_blank">Deep Learning Face Attributes in the Wild</a></td><td><a href="https://arxiv.org/pdf/1411.7766.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>52%</td><td>808</td><td>424</td><td>383</td><td>68</td><td>670</td><td>118</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>742</td><td>408</td><td>332</td><td>59</td><td>416</td><td>329</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>742</td><td>408</td><td>332</td><td>59</td><td>416</td><td>329</td></tr><tr><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/32cde90437ab5a70cf003ea36f66f2de0e24b3ab.html" target="_blank">The Cityscapes Dataset for Semantic Urban Scene Understanding</a></td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>771</td><td>407</td><td>364</td><td>54</td><td>624</td><td>138</td></tr><tr><td>177bc509dd0c7b8d388bb47403f28d6228c14b5c</td><td>celeba_plus</td><td>CelebFaces+</td><td><a href="papers/177bc509dd0c7b8d388bb47403f28d6228c14b5c.html" target="_blank">Deep Learning Face Representation from Predicting 10,000 Classes</a></td><td><a href="http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf" target="_blank">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>660</td><td>405</td><td>255</td><td>25</td><td>340</td><td>330</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html" target="_blank">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><a href="http://http.cs.berkeley.edu/Research/Projects/CS/vision/human/poselets_iccv09.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>707</td><td>374</td><td>333</td><td>67</td><td>509</td><td>215</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html" target="_blank">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>584</td><td>335</td><td>249</td><td>38</td><td>338</td><td>245</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfw_p</td><td>LFWP</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html" target="_blank">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><a href="http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>521</td><td>321</td><td>200</td><td>42</td><td>337</td><td>195</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk03</td><td>CUHK03</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>512</td><td>306</td><td>206</td><td>29</td><td>324</td><td>180</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html" target="_blank">Face recognition in unconstrained videos with matched background similarity</a></td><td><a href="http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Tel Aviv University</td><td>Israel</td><td>32.11198890</td><td>34.80459702</td><td>62%</td><td>485</td><td>299</td><td>185</td><td>30</td><td>298</td><td>193</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html" target="_blank">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="https://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>586</td><td>296</td><td>288</td><td>48</td><td>345</td><td>244</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>529</td><td>292</td><td>236</td><td>40</td><td>324</td><td>213</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>529</td><td>292</td><td>236</td><td>40</td><td>324</td><td>213</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>529</td><td>292</td><td>236</td><td>40</td><td>324</td><td>213</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html" target="_blank">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" target="_blank">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>555</td><td>275</td><td>279</td><td>47</td><td>299</td><td>270</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html" target="_blank">Pedestrian detection: A benchmark</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1378.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>519</td><td>265</td><td>254</td><td>27</td><td>289</td><td>233</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html" target="_blank">Learning Face Representation from Scratch</a></td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese Academy of Sciences</td><td>China</td><td>40.00447950</td><td>116.37023800</td><td>60%</td><td>436</td><td>263</td><td>173</td><td>30</td><td>288</td><td>150</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html" target="_blank">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="http://facedetection.homepage.t-online.de/downloads/AVBPA01BioID.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>498</td><td>249</td><td>249</td><td>56</td><td>330</td><td>179</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>424</td><td>239</td><td>184</td><td>26</td><td>239</td><td>190</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>424</td><td>239</td><td>184</td><td>26</td><td>239</td><td>190</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html" target="_blank">Scalable Person Re-identification: A Benchmark</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>394</td><td>238</td><td>156</td><td>18</td><td>272</td><td>116</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>383</td><td>233</td><td>150</td><td>25</td><td>265</td><td>121</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>383</td><td>233</td><td>150</td><td>25</td><td>265</td><td>121</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html" target="_blank">Web-based database for facial expression analysis</a></td><td><a href="http://dev.pubs.doc.ic.ac.uk/Pantic-ICME05-2/Pantic-ICME05-2.pdf" target="_blank">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>440</td><td>223</td><td>217</td><td>44</td><td>267</td><td>181</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html" target="_blank">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><a href="http://ei.is.tuebingen.mpg.de/uploads_file/attachment/attachment/168/andriluka14benchmark.pdf" target="_blank">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>356</td><td>221</td><td>135</td><td>21</td><td>304</td><td>53</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>373</td><td>217</td><td>155</td><td>35</td><td>251</td><td>129</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>373</td><td>217</td><td>155</td><td>35</td><td>251</td><td>129</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>373</td><td>217</td><td>155</td><td>35</td><td>251</td><td>129</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html" target="_blank">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><a href="http://www.jdl.ac.cn/peal/files/ieee_smc_a_gao_cas-peal.pdf" target="_blank">[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></td><td>51%</td><td>415</td><td>212</td><td>203</td><td>39</td><td>189</td><td>232</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html" target="_blank">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>352</td><td>206</td><td>146</td><td>27</td><td>196</td><td>157</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html" target="_blank">Interactive Facial Feature Localization</a></td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>59%</td><td>339</td><td>201</td><td>138</td><td>29</td><td>219</td><td>129</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html" target="_blank">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>324</td><td>199</td><td>125</td><td>29</td><td>211</td><td>118</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html" target="_blank">Robust Face Landmark Estimation under Occlusion</a></td><td><a href="http://authors.library.caltech.edu/45988/1/ICCV13%20Burgos-Artizzu.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>62%</td><td>305</td><td>188</td><td>117</td><td>16</td><td>192</td><td>116</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>278</td><td>182</td><td>96</td><td>13</td><td>208</td><td>78</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>278</td><td>182</td><td>96</td><td>13</td><td>208</td><td>78</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html" target="_blank">Presentation and validation of the Radboud Faces Database</a></td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>446</td><td>180</td><td>266</td><td>43</td><td>322</td><td>136</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html" target="_blank">Depth and Appearance for Mobile Scene Analysis</a></td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07-poster.pdf" target="_blank">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>56%</td><td>319</td><td>179</td><td>140</td><td>27</td><td>195</td><td>127</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html" target="_blank">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><a href="http://gravis.cs.unibas.ch/publications/2009/BFModel09.pdf" target="_blank">[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></td><td>55%</td><td>323</td><td>178</td><td>145</td><td>29</td><td>226</td><td>98</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html" target="_blank">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><a href="http://iris.usc.edu/Outlines/papers/2009/yuan-chang-nevatia-cvpr09.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Southern California</td><td>United States</td><td>34.02241490</td><td>-118.28634407</td><td>53%</td><td>330</td><td>176</td><td>153</td><td>27</td><td>196</td><td>139</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html" target="_blank">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><a href="http://face.cs.kit.edu/befit/workshop2011/pdf/slides/martin_koestinger-slides.pdf" target="_blank">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>292</td><td>175</td><td>117</td><td>37</td><td>212</td><td>84</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>55%</td><td>302</td><td>166</td><td>136</td><td>34</td><td>207</td><td>100</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>55%</td><td>302</td><td>166</td><td>136</td><td>34</td><td>207</td><td>100</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html" target="_blank">Bosphorus Database for 3D Face Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>328</td><td>165</td><td>163</td><td>19</td><td>149</td><td>183</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces_news</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html" target="_blank">Names and faces in the news</a></td><td><a href="http://ttic.uchicago.edu/~mmaire/papers/pdf/names_faces_cvpr2004.pdf" target="_blank">[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></td><td>56%</td><td>294</td><td>165</td><td>128</td><td>29</td><td>215</td><td>82</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk01</td><td>CUHK01</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html" target="_blank">Human Reidentification with Transferred Metric Learning</a></td><td><a href="https://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>258</td><td>161</td><td>97</td><td>12</td><td>142</td><td>115</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html" target="_blank">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><a href="http://static.cs.brown.edu/~gen/pub_papers/SUN_Attribute_Database_CVPR2012.pdf" target="_blank">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Brown University</td><td>United States</td><td>41.82686820</td><td>-71.40123146</td><td>58%</td><td>269</td><td>156</td><td>113</td><td>29</td><td>215</td><td>57</td></tr><tr><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/010f0f4929e6a6644fb01f0e43820f91d0fad292.html" target="_blank">YFCC100M: the new data in multimedia research</a></td><td><a href="https://arxiv.org/pdf/1503.01817.pdf" target="_blank">[pdf]</a></td><td>Commun. ACM</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>37.41021930</td><td>-122.05965487</td><td>56%</td><td>276</td><td>155</td><td>121</td><td>23</td><td>175</td><td>99</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>towncenter</td><td>TownCenter</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html" target="_blank">Stable multi-target tracking in real-time surveillance video</a></td><td><a href="http://ben.benfold.com/docs/benfold_reid_cvpr2011-preprint.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>310</td><td>142</td><td>168</td><td>24</td><td>180</td><td>131</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html" target="_blank">Generic object recognition with boosting</a></td><td><a href="http://www.cse.unr.edu/~bebis/CS773C/ObjectRecognition/Papers/Opelt06.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>49%</td><td>286</td><td>141</td><td>145</td><td>16</td><td>193</td><td>97</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html" target="_blank">Recognition using visual phrases</a></td><td><a href="http://vision.cs.uiuc.edu/phrasal/recognition_using_visual_phrases.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>233</td><td>136</td><td>97</td><td>18</td><td>177</td><td>58</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk02</td><td>CUHK02</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>242</td><td>130</td><td>112</td><td>17</td><td>139</td><td>102</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html" target="_blank">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>218</td><td>126</td><td>91</td><td>17</td><td>152</td><td>71</td></tr><tr><td>0c91808994a250d7be332400a534a9291ca3b60e</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/0c91808994a250d7be332400a534a9291ca3b60e.html" target="_blank">Weak Hypotheses and Boosting for Generic Object Detection and Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>247</td><td>125</td><td>122</td><td>18</td><td>177</td><td>78</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>berkeley_pose</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html" target="_blank">Describing people: A poselet-based approach to attribute classification</a></td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>221</td><td>125</td><td>96</td><td>14</td><td>165</td><td>59</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_c</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html" target="_blank">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Klareetal_UnconstrainedFaceDetectionRecognitionJanus_CVPR15.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>222</td><td>123</td><td>99</td><td>19</td><td>161</td><td>62</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>ilids_vid_reid</td><td>iLIDS-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>210</td><td>122</td><td>88</td><td>10</td><td>115</td><td>94</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>210</td><td>122</td><td>88</td><td>10</td><td>115</td><td>94</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>217</td><td>121</td><td>96</td><td>14</td><td>133</td><td>86</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>217</td><td>121</td><td>96</td><td>14</td><td>133</td><td>86</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html" target="_blank">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>198</td><td>117</td><td>81</td><td>16</td><td>111</td><td>88</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html" target="_blank">Exploring Models and Data for Image Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>191</td><td>116</td><td>75</td><td>12</td><td>165</td><td>27</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html" target="_blank">Understanding images of groups of people</a></td><td><a href="http://chenlab.ece.cornell.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>37.41021930</td><td>-122.05965487</td><td>56%</td><td>202</td><td>113</td><td>89</td><td>12</td><td>132</td><td>75</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html" target="_blank">Learning effective human pose estimation from inaccurate annotation</a></td><td><a href="http://www.comp.leeds.ac.uk/mat4saj/publications/johnson11cvpr.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Leeds</td><td>United Kingdom</td><td>53.80387185</td><td>-1.55245712</td><td>65%</td><td>173</td><td>112</td><td>61</td><td>10</td><td>122</td><td>56</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>185</td><td>111</td><td>74</td><td>15</td><td>124</td><td>64</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html" target="_blank">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><a href="http://users.cecs.anu.edu.au/~adhall/Dhall_Goecke_Lucey_Gedeon_M_2012.pdf" target="_blank">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>Australia</td><td>-35.27769990</td><td>149.11852700</td><td>61%</td><td>182</td><td>111</td><td>71</td><td>8</td><td>86</td><td>99</td></tr><tr><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/570f37ed63142312e6ccdf00ecc376341ec72b9f.html" target="_blank">Social LSTM: Human Trajectory Prediction in Crowded Spaces</a></td><td><a href="http://cs.stanford.edu/groups/vision/pdf/CVPR16_N_LSTM.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>229</td><td>106</td><td>123</td><td>5</td><td>150</td><td>79</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html" target="_blank">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><a href="http://michaelryoo.com/papers/cvpr2013_ryoo.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>148</td><td>103</td><td>45</td><td>8</td><td>111</td><td>38</td></tr><tr><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/22ad2c8c0f4d6aa4328b38d894b814ec22579761.html" target="_blank">Clothing cosegmentation for recognizing people</a></td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/2670CVPR08Gallagher.pdf" target="_blank">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>37.41021930</td><td>-122.05965487</td><td>58%</td><td>177</td><td>103</td><td>74</td><td>7</td><td>101</td><td>84</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw_a</td><td>LFW-a</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html" target="_blank">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/jpatchlbp.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>177</td><td>101</td><td>76</td><td>15</td><td>104</td><td>75</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html" target="_blank">Age and Gender Estimation of Unfiltered Faces</a></td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>60%</td><td>168</td><td>101</td><td>67</td><td>5</td><td>94</td><td>78</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html" target="_blank">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><a href="http://personal.ie.cuhk.edu.hk/~lz013/papers/deepfashion_poster.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>150</td><td>97</td><td>53</td><td>4</td><td>111</td><td>38</td></tr><tr><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td><td>scface</td><td>SCface</td><td><a href="papers/29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d.html" target="_blank">SCface – surveillance cameras face database</a></td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td>Multimedia Tools and Applications</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>178</td><td>97</td><td>81</td><td>15</td><td>90</td><td>89</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html" target="_blank">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><a href="http://www.iainm.com/iainm/Publications_files/2011_PAINFUL.pdf" target="_blank">[pdf]</a></td><td>Face and Gesture 2011</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>37.41021930</td><td>-122.05965487</td><td>52%</td><td>184</td><td>96</td><td>88</td><td>23</td><td>112</td><td>71</td></tr><tr><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td><td>disfa</td><td>DISFA</td><td><a href="papers/5a5f0287484f0d480fed1ce585dbf729586f0edc.html" target="_blank">DISFA: A Spontaneous Facial Action Intensity Database</a></td><td><a href="http://mohammadmahoor.com/wp-content/uploads/2017/06/DiSFA_Paper_andAppendix_Final_OneColumn1-1.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Affective Computing</td><td>edu</td><td>University of Denver</td><td>United States</td><td>39.67665410</td><td>-104.96220300</td><td>51%</td><td>190</td><td>96</td><td>94</td><td>19</td><td>100</td><td>91</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html" target="_blank">Appearance-based gaze estimation in the wild</a></td><td><a href="https://arxiv.org/pdf/1504.02863.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>138</td><td>92</td><td>46</td><td>3</td><td>97</td><td>42</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html" target="_blank">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>167</td><td>91</td><td>76</td><td>14</td><td>131</td><td>36</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html" target="_blank">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><a href="http://crcv-web.eecs.ucf.edu/papers/cvpr2013/Counting_V3o.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>125</td><td>89</td><td>36</td><td>6</td><td>73</td><td>52</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html" target="_blank">Multi-camera activity correlation analysis</a></td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>64%</td><td>138</td><td>89</td><td>49</td><td>8</td><td>79</td><td>61</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html" target="_blank">On a Large Sequence-Based Human Gait Database</a></td><td><a href="https://eprints.soton.ac.uk/257901/1/Shutler_2002.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>148</td><td>88</td><td>60</td><td>17</td><td>104</td><td>49</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html" target="_blank">WIDER FACE: A Face Detection Benchmark</a></td><td><a href="https://arxiv.org/pdf/1511.06523.pdf" target="_blank">[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>China</td><td>22.41626320</td><td>114.21093180</td><td>58%</td><td>148</td><td>86</td><td>62</td><td>15</td><td>108</td><td>41</td></tr><tr><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td><td>fei</td><td>FEI</td><td><a href="papers/8b56e33f33e582f3e473dba573a16b598ed9bcdc.html" target="_blank">A new ranking method for principal components analysis and its application to face image analysis</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>169</td><td>86</td><td>83</td><td>6</td><td>72</td><td>101</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html" target="_blank">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="http://liangzheng.org/1320.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>146</td><td>85</td><td>61</td><td>6</td><td>97</td><td>49</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>136</td><td>79</td><td>57</td><td>7</td><td>108</td><td>27</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>136</td><td>79</td><td>57</td><td>7</td><td>108</td><td>27</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html" target="_blank">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><a href="http://vc.cs.nthu.edu.tw/home/paper/codfiles/htchiang/201212250411/newp12.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>151</td><td>78</td><td>73</td><td>9</td><td>79</td><td>73</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html" target="_blank">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><a href="http://www.csee.usf.edu/~scanavan/papers/FG2013.pdf" target="_blank">[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>United States</td><td>42.08779975</td><td>-75.97066066</td><td>52%</td><td>151</td><td>78</td><td>73</td><td>7</td><td>87</td><td>65</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html" target="_blank">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>55%</td><td>133</td><td>73</td><td>60</td><td>13</td><td>94</td><td>41</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html" target="_blank">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Massachusetts</td><td>United States</td><td>42.38897850</td><td>-72.52869870</td><td>59%</td><td>123</td><td>73</td><td>50</td><td>3</td><td>72</td><td>50</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html" target="_blank">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><a href="https://arxiv.org/pdf/1304.0869.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>128</td><td>71</td><td>57</td><td>6</td><td>73</td><td>60</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html" target="_blank">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" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>112</td><td>71</td><td>41</td><td>14</td><td>84</td><td>29</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html" target="_blank">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>120</td><td>71</td><td>49</td><td>5</td><td>74</td><td>47</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html" target="_blank">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><a href="https://arxiv.org/pdf/1512.00596.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>121</td><td>71</td><td>50</td><td>9</td><td>98</td><td>22</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html" target="_blank">A data-driven approach to cleaning large face datasets</a></td><td><a href="http://stefan.winkler.net/Publications/icip2014a.pdf" target="_blank">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>123</td><td>66</td><td>57</td><td>4</td><td>96</td><td>27</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html" target="_blank">Hipster wars: Discovering elements of fashion styles</a></td><td><a href="http://acberg.com/papers/hipster_eccv14.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>91</td><td>62</td><td>29</td><td>5</td><td>61</td><td>29</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html" target="_blank">Understanding Kin Relationships in a Photo</a></td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>96</td><td>61</td><td>35</td><td>2</td><td>34</td><td>63</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html" target="_blank">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>122</td><td>61</td><td>61</td><td>11</td><td>71</td><td>51</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html" target="_blank">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>114</td><td>61</td><td>53</td><td>10</td><td>71</td><td>43</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>110</td><td>60</td><td>50</td><td>12</td><td>69</td><td>43</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>110</td><td>60</td><td>50</td><td>12</td><td>69</td><td>43</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html" target="_blank">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>90</td><td>57</td><td>33</td><td>5</td><td>60</td><td>31</td></tr><tr><td>9a9877791945c6fa4c1743ec6d3fb32570ef8481</td><td>m2vts</td><td>m2vts</td><td><a href="papers/9a9877791945c6fa4c1743ec6d3fb32570ef8481.html" target="_blank">The M2VTS Multimodal Face Database (Release 1.00)</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Laboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, Belgium</td><td>Belgium</td><td>50.66968750</td><td>4.61559090</td><td>44%</td><td>129</td><td>57</td><td>72</td><td>4</td><td>80</td><td>54</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html" target="_blank">Pedestrian Attribute Recognition At Far Distance</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>80</td><td>55</td><td>25</td><td>2</td><td>51</td><td>28</td></tr><tr><td>06f02199690961ba52997cde1527e714d2b3bf8f</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/06f02199690961ba52997cde1527e714d2b3bf8f.html" target="_blank">Gaze locking: passive eye contact detection for human-object interaction</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>66%</td><td>80</td><td>53</td><td>27</td><td>0</td><td>49</td><td>35</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html" target="_blank">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Liu_A_Spatio-Temporal_Appearance_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>85</td><td>53</td><td>32</td><td>9</td><td>51</td><td>34</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html" target="_blank">Object Detection Combining Recognition and Segmentation</a></td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>101</td><td>52</td><td>49</td><td>11</td><td>58</td><td>42</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html" target="_blank">We are family: joint pose estimation of multiple persons</a></td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>77</td><td>51</td><td>26</td><td>5</td><td>60</td><td>19</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html" target="_blank">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>112</td><td>50</td><td>62</td><td>11</td><td>79</td><td>35</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html" target="_blank">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><a href="http://www.eurecom.fr/fr/publication/4393/download/mm-publi-4393.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td>edu</td><td>University of North Carolina at Chapel Hill</td><td>United States</td><td>35.91139710</td><td>-79.05045290</td><td>64%</td><td>75</td><td>48</td><td>27</td><td>6</td><td>26</td><td>50</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html" target="_blank">High Five: Recognising human interactions in TV shows</a></td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>91</td><td>47</td><td>44</td><td>11</td><td>64</td><td>27</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html" target="_blank">Violent flows: Real-time detection of violent crowd behavior</a></td><td><a href="http://www.openu.ac.il/home/hassner/data/violentflows/violent_flows.pdf" target="_blank">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>57%</td><td>83</td><td>47</td><td>36</td><td>6</td><td>44</td><td>41</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html" target="_blank">Labeled Faces in the Wild : A Survey</a></td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Stevens Institute of Technology</td><td>United States</td><td>40.74225200</td><td>-74.02709490</td><td>47%</td><td>99</td><td>47</td><td>52</td><td>8</td><td>63</td><td>36</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html" target="_blank">Automatic 3D face authentication</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>95</td><td>45</td><td>50</td><td>8</td><td>61</td><td>35</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html" target="_blank">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>52%</td><td>83</td><td>43</td><td>40</td><td>6</td><td>51</td><td>33</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html" target="_blank">Kinship verification through transfer learning</a></td><td><a href="http://ijcai.org/Proceedings/11/Papers/422.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>71</td><td>43</td><td>28</td><td>2</td><td>29</td><td>43</td></tr><tr><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td><td>put_face</td><td>Put Face</td><td><a href="papers/ae0aee03d946efffdc7af2362a42d3750e7dd48a.html" target="_blank">The put face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>100</td><td>43</td><td>57</td><td>7</td><td>56</td><td>48</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html" target="_blank">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><a href="https://arxiv.org/pdf/1502.00739.pdf" target="_blank">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>60</td><td>42</td><td>18</td><td>0</td><td>38</td><td>24</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html" target="_blank">Pruning training sets for learning of object categories</a></td><td><a href="http://authors.library.caltech.edu/11469/1/ANGcvpr05.pdf" target="_blank">[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></td><td>68%</td><td>60</td><td>41</td><td>19</td><td>5</td><td>43</td><td>17</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html" target="_blank">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><a href="http://cbcsl.ece.ohio-state.edu/cvpr16.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>72</td><td>41</td><td>31</td><td>7</td><td>54</td><td>17</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html" target="_blank">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>83</td><td>40</td><td>43</td><td>2</td><td>63</td><td>19</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html" target="_blank">FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</a></td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>82</td><td>38</td><td>44</td><td>5</td><td>62</td><td>20</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html" target="_blank">Texas 3D Face Recognition Database</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf" target="_blank">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>61</td><td>38</td><td>23</td><td>3</td><td>37</td><td>25</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html" target="_blank">Automated Human Identification Using Ear Imaging</a></td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>70</td><td>38</td><td>32</td><td>6</td><td>28</td><td>42</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html" target="_blank">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><a href="https://arxiv.org/pdf/1501.05703.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>74%</td><td>50</td><td>37</td><td>12</td><td>2</td><td>40</td><td>9</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html" target="_blank">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><a href="http://affect.media.mit.edu/pdfs/13.McDuff-etal-AMFED.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>49%</td><td>73</td><td>36</td><td>37</td><td>6</td><td>41</td><td>34</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html" target="_blank">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>68</td><td>36</td><td>32</td><td>8</td><td>49</td><td>17</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf" target="_blank">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>78%</td><td>46</td><td>36</td><td>10</td><td>1</td><td>18</td><td>28</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf" target="_blank">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>78%</td><td>46</td><td>36</td><td>10</td><td>1</td><td>18</td><td>28</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>69%</td><td>49</td><td>34</td><td>15</td><td>0</td><td>18</td><td>31</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>69%</td><td>49</td><td>34</td><td>15</td><td>0</td><td>18</td><td>31</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html" target="_blank">The MUG facial expression database</a></td><td><span class="gray">[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>Greece</td><td>40.62984145</td><td>22.95889350</td><td>50%</td><td>68</td><td>34</td><td>34</td><td>5</td><td>28</td><td>40</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html" target="_blank">Person Re-identification in the Wild</a></td><td><a href="https://arxiv.org/pdf/1604.02531.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>65</td><td>33</td><td>32</td><td>1</td><td>46</td><td>17</td></tr><tr><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td><td>nudedetection</td><td>Nude Detection</td><td><a href="papers/7ace44190729927e5cb0dd5d363fcae966fe13f7.html" target="_blank">A bag-of-features approach based on Hue-SIFT descriptor for nude detection</a></td><td><a href="http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569191772.pdf" target="_blank">[pdf]</a></td><td>2009 17th European Signal Processing Conference</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>51</td><td>33</td><td>18</td><td>1</td><td>18</td><td>33</td></tr><tr><td>5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2</td><td>cityscapes</td><td>Cityscapes</td><td><a href="papers/5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2.html" target="_blank">The Cityscapes Dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>54</td><td>32</td><td>22</td><td>3</td><td>40</td><td>14</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>52</td><td>30</td><td>22</td><td>5</td><td>37</td><td>15</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html" target="_blank">Recognize complex events from static images by fusing deep channels</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2015/supplemental/Xiong_Recognize_Complex_Events_2015_CVPR_supplemental.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>45</td><td>30</td><td>15</td><td>1</td><td>30</td><td>15</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html" target="_blank">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><a href="http://allenai.org/content/publications/SituationRecognition.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>48</td><td>30</td><td>18</td><td>2</td><td>46</td><td>2</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html" target="_blank">UMB-DB: A database of partially occluded 3D faces</a></td><td><a href="http://face.cs.kit.edu/befit/workshop2011/pdf/slides/claudio_cusano-slides.pdf" target="_blank">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>45</td><td>29</td><td>16</td><td>2</td><td>20</td><td>24</td></tr><tr><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/18858cc936947fc96b5c06bbe3c6c2faa5614540.html" target="_blank">Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</a></td><td><a href="https://pdfs.semanticscholar.org/03c1/fc9c3339813ed81ad0de540132f9f695a0f8.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>55</td><td>29</td><td>26</td><td>0</td><td>47</td><td>7</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="http://klab.tch.harvard.edu/academia/classes/Neuro230/2012/lectures/Lecture_11_Reading.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>58%</td><td>50</td><td>29</td><td>21</td><td>3</td><td>39</td><td>11</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><a href="http://klab.tch.harvard.edu/academia/classes/Neuro230/2012/lectures/Lecture_11_Reading.pdf" target="_blank">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>58%</td><td>50</td><td>29</td><td>21</td><td>3</td><td>39</td><td>11</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html" target="_blank">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" target="_blank">[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></td><td>47%</td><td>58</td><td>27</td><td>31</td><td>7</td><td>41</td><td>18</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html" target="_blank">The intrinsic memorability of face photographs.</a></td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>47</td><td>25</td><td>22</td><td>3</td><td>34</td><td>13</td></tr><tr><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td><td>pornodb</td><td>Pornography DB</td><td><a href="papers/b92a1ed9622b8268ae3ac9090e25789fc41cc9b8.html" target="_blank">Pooling in image representation: The visual codeword point of view</a></td><td><a href="http://cedric.cnam.fr/~thomen/papers/avila_CVIU2012_final.pdf" target="_blank">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>32%</td><td>77</td><td>25</td><td>52</td><td>7</td><td>46</td><td>34</td></tr><tr><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/eb027969f9310e0ae941e2adee2d42cdf07d938c.html" target="_blank">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>45%</td><td>56</td><td>25</td><td>31</td><td>6</td><td>50</td><td>6</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html" target="_blank">Consistent Re-identification in a Camera Network</a></td><td><a href="http://cs-people.bu.edu/dasabir/papers/ECCV14_Poster.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>45</td><td>24</td><td>21</td><td>7</td><td>34</td><td>11</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html" target="_blank">The CMU Face In Action (FIA) Database</a></td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>55</td><td>24</td><td>31</td><td>5</td><td>41</td><td>17</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html" target="_blank">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>44</td><td>23</td><td>21</td><td>0</td><td>36</td><td>7</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html" target="_blank">End-to-End Deep Learning for Person Search</a></td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>41</td><td>22</td><td>19</td><td>2</td><td>27</td><td>12</td></tr><tr><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/758d7e1be64cc668c59ef33ba8882c8597406e53.html" target="_blank">AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</a></td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>38</td><td>22</td><td>16</td><td>1</td><td>26</td><td>11</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html" target="_blank">Personalizing Human Video Pose Estimation</a></td><td><a href="https://arxiv.org/pdf/1511.06676.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>66%</td><td>32</td><td>21</td><td>11</td><td>2</td><td>29</td><td>5</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html" target="_blank">Re-identify people in wide area camera network</a></td><td><a href="http://users.dimi.uniud.it/~niki.martinel/data/publications/2012/CVPR/MarMicCVPR2012.pdf" target="_blank">[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></td><td>38%</td><td>55</td><td>21</td><td>34</td><td>2</td><td>35</td><td>19</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html" target="_blank">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" target="_blank">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>39</td><td>21</td><td>18</td><td>2</td><td>27</td><td>12</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html" target="_blank">UMDFaces: An annotated face dataset for training deep networks</a></td><td><a href="https://arxiv.org/pdf/1611.01484.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td>edu</td><td>University of Maryland</td><td>United States</td><td>39.28996850</td><td>-76.62196103</td><td>57%</td><td>35</td><td>20</td><td>15</td><td>4</td><td>28</td><td>7</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html" target="_blank">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>40</td><td>20</td><td>20</td><td>0</td><td>21</td><td>20</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html" target="_blank">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>76%</td><td>25</td><td>19</td><td>6</td><td>1</td><td>19</td><td>7</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html" target="_blank">Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</a></td><td><a href="https://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>44</td><td>19</td><td>25</td><td>1</td><td>21</td><td>23</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html" target="_blank">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td>edu</td><td>University of Notre Dame</td><td>United States</td><td>41.70456775</td><td>-86.23822026</td><td>59%</td><td>32</td><td>19</td><td>13</td><td>3</td><td>17</td><td>15</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html" target="_blank">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><a href="https://arxiv.org/pdf/1608.01041.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>29</td><td>18</td><td>11</td><td>0</td><td>15</td><td>14</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>1</td><td>13</td><td>13</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>1</td><td>13</td><td>13</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html" target="_blank">Fashion Landmark Detection in the Wild</a></td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>1</td><td>17</td><td>9</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html" target="_blank">Ear Recognition: More Than a Survey</a></td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>24</td><td>17</td><td>7</td><td>0</td><td>11</td><td>13</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html" target="_blank">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Chu_Multi-Task_Recurrent_Neural_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>25</td><td>16</td><td>9</td><td>2</td><td>21</td><td>5</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>CAFE</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html" target="_blank">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td></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>5</td></tr><tr><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/a5a44a32a91474f00a3cda671a802e87c899fbb4.html" target="_blank">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>25</td><td>16</td><td>9</td><td>2</td><td>25</td><td>0</td></tr><tr><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td><td>buhmap_db</td><td>BUHMAP-DB</td><td><a href="papers/014b8df0180f33b9fea98f34ae611c6447d761d2.html" target="_blank">Facial feature tracking and expression recognition for sign language</a></td><td><a href="https://www.cmpe.boun.edu.tr/~ari/files/ari2008iscis.pdf" target="_blank">[pdf]</a></td><td>2009 IEEE 17th Signal Processing and Communications Applications Conference</td><td></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>11</td><td>15</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>27</td><td>15</td><td>12</td><td>1</td><td>23</td><td>5</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>27</td><td>15</td><td>12</td><td>1</td><td>23</td><td>5</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html" target="_blank">Level Playing Field for Million Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>United States</td><td>47.65432380</td><td>-122.30800894</td><td>39%</td><td>38</td><td>15</td><td>23</td><td>2</td><td>29</td><td>8</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html" target="_blank">Describing Common Human Visual Actions in Images</a></td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>26</td><td>15</td><td>11</td><td>0</td><td>25</td><td>1</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html" target="_blank">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>21</td><td>14</td><td>7</td><td>0</td><td>18</td><td>3</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html" target="_blank">Genealogical face recognition based on UB KinFace database</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>United States</td><td>42.93362780</td><td>-78.88394479</td><td>47%</td><td>30</td><td>14</td><td>16</td><td>1</td><td>10</td><td>21</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html" target="_blank">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>18</td><td>14</td><td>4</td><td>0</td><td>16</td><td>2</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td>i-LIDS</td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html" target="_blank">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></td><td>41%</td><td>32</td><td>13</td><td>19</td><td>2</td><td>18</td><td>15</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>50_people_one_question</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html" target="_blank">Merging Pose Estimates Across Space and Time</a></td><td><a href="https://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>87%</td><td>15</td><td>13</td><td>2</td><td>0</td><td>12</td><td>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></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>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></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>4</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html" target="_blank">Object recognition using segmentation for feature detection</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td>edu</td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>Austria</td><td>47.38473720</td><td>15.09302010</td><td>41%</td><td>29</td><td>12</td><td>17</td><td>1</td><td>21</td><td>8</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html" target="_blank">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" target="_blank">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>36</td><td>12</td><td>24</td><td>4</td><td>25</td><td>11</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html" target="_blank">Fine-grained evaluation on face detection in the wild</a></td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></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>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html" target="_blank">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><a href="http://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/papers/Shankar2013Indian.pdf" target="_blank">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td>edu</td><td>India</td><td></td><td>20.59368400</td><td>78.96288000</td><td>80%</td><td>15</td><td>12</td><td>3</td><td>0</td><td>10</td><td>5</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html" target="_blank">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>41%</td><td>29</td><td>12</td><td>17</td><td>3</td><td>17</td><td>12</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html" target="_blank">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="https://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>17</td><td>11</td><td>6</td><td>1</td><td>12</td><td>5</td></tr><tr><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/a8d0b149c2eadaa02204d3e4356fbc8eccf3b315.html" target="_blank">Hi4D-ADSIP 3-D dynamic facial articulation database</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>18</td><td>11</td><td>7</td><td>1</td><td>7</td><td>11</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html" target="_blank">Recognizing disguised faces</a></td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>42%</td><td>24</td><td>10</td><td>14</td><td>0</td><td>18</td><td>6</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html" target="_blank">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>United States</td><td>34.22498270</td><td>-77.86907744</td><td>77%</td><td>13</td><td>10</td><td>3</td><td>0</td><td>6</td><td>8</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html" target="_blank">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>21</td><td>10</td><td>11</td><td>3</td><td>11</td><td>10</td></tr><tr><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td><td>agedb</td><td>AgeDB</td><td><a href="papers/6dcf418c778f528b5792104760f1fbfe90c6dd6a.html" target="_blank">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf" target="_blank">[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></td><td>91%</td><td>11</td><td>10</td><td>1</td><td>0</td><td>10</td><td>1</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html" target="_blank">Iranian Face Database with age, pose and expression</a></td><td><a href="http://www.iranprc.org/pdf/paper/2007-02.pdf" target="_blank">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td>edu</td><td>Islamic Azad University</td><td>Iran</td><td>34.84529990</td><td>48.55962120</td><td>50%</td><td>20</td><td>10</td><td>10</td><td>2</td><td>12</td><td>9</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html" target="_blank">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><span class="gray">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>Canada</td><td>45.50397610</td><td>-73.57496870</td><td>56%</td><td>18</td><td>10</td><td>8</td><td>0</td><td>13</td><td>7</td></tr><tr><td>ec792ad2433b6579f2566c932ee414111e194537</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/ec792ad2433b6579f2566c932ee414111e194537.html" target="_blank">Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</a></td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>14</td><td>10</td><td>4</td><td>1</td><td>11</td><td>3</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html" target="_blank">Learning Social Relation Traits from Face Images</a></td><td><a href="https://arxiv.org/pdf/1509.03936.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>5</td><td>15</td><td>5</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html" target="_blank">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>2</td><td>17</td><td>4</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html" target="_blank">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>25</td><td>9</td><td>16</td><td>3</td><td>17</td><td>6</td></tr><tr><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2b926b3586399d028b46315d7d9fb9d879e4f79c.html" target="_blank">Multimodal 2D, 2.5D & 3D Face Verification</a></td><td><a href="http://www.researchgate.net/profile/Enrique_Cabello/publication/224057733_Multimodal_2D_2.5D__3D_Face_Verification/links/0912f50f522298fa95000000.pdf" target="_blank">[pdf]</a></td><td>2006 International Conference on Image Processing</td><td>edu</td><td>Universidad Rey Juan Carlos, Spain</td><td></td><td>40.33586610</td><td>-3.87694320</td><td>57%</td><td>14</td><td>8</td><td>6</td><td>0</td><td>2</td><td>12</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html" target="_blank">YawDD: a yawning detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>14</td><td>8</td><td>6</td><td>1</td><td>2</td><td>12</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_c</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html" target="_blank">IARPA Janus Benchmark-B Face Dataset</a></td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Whitelametal_IARPAJanusBenchmark-BFaceDataset_CVPRW17.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Michigan State University</td><td>United States</td><td>42.71856800</td><td>-84.47791571</td><td>28%</td><td>25</td><td>7</td><td>18</td><td>6</td><td>21</td><td>4</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html" target="_blank">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>9</td><td>7</td><td>2</td><td>0</td><td>5</td><td>4</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html" target="_blank">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></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>4</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html" target="_blank">VADANA: A dense dataset for facial image analysis</a></td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>University of Delaware</td><td>United States</td><td>39.68103280</td><td>-75.75401840</td><td>44%</td><td>16</td><td>7</td><td>9</td><td>0</td><td>6</td><td>10</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html" target="_blank">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</a></td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td>IEEE transactions on pattern analysis and machine intelligence</td><td></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>5</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html" target="_blank">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><a href="https://arxiv.org/pdf/1511.02459.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>14</td><td>7</td><td>7</td><td>3</td><td>5</td><td>9</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html" target="_blank">How to Take a Good Selfie?</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>9</td><td>6</td><td>3</td><td>0</td><td>6</td><td>4</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html" target="_blank">Component-Based Face Recognition with 3D Morphable Models</a></td><td><a href="http://cbcl.mit.edu/cbcl/publications/theses/thesis-huang.pdf" target="_blank">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>12</td><td>6</td><td>6</td><td>0</td><td>8</td><td>4</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html" target="_blank">Competitive affective gaming: winning with a smile</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>Portugal</td><td>38.66096400</td><td>-9.20581300</td><td>75%</td><td>8</td><td>6</td><td>2</td><td>0</td><td>4</td><td>4</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html" target="_blank">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ECCV_2014/workshops/w19/11%20-%20The%20HDA%20data%20set%20for%20research%20on%20fully.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>35%</td><td>17</td><td>6</td><td>11</td><td>2</td><td>11</td><td>6</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html" target="_blank">Large age-gap face verification by feature injection in deep networks</a></td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html" target="_blank">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="http://www.csis.pace.edu/~ctappert/dps/2013BTAS/Papers/Paper%20139/PID2859389.pdf" target="_blank">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>1</td><td>3</td><td>5</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html" target="_blank">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><a href="http://cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf" target="_blank">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>83%</td><td>6</td><td>5</td><td>1</td><td>1</td><td>5</td><td>1</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html" target="_blank">USED: a large-scale social event detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Trento</td><td>Italy</td><td>46.06588360</td><td>11.11598940</td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html" target="_blank">ChaLearn looking at people: A review of events and resources</a></td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>10</td><td>5</td><td>5</td><td>1</td><td>6</td><td>4</td></tr><tr><td>a5a3bc3e5e9753769163cb30b16dbd12e266b93e</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/a5a3bc3e5e9753769163cb30b16dbd12e266b93e.html" target="_blank">Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>8</td><td>5</td><td>3</td><td>1</td><td>5</td><td>3</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html" target="_blank">Large scale unconstrained open set face database</a></td><td><a href="http://vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf" target="_blank">[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>United States</td><td>38.89646790</td><td>-104.80505940</td><td>80%</td><td>5</td><td>4</td><td>1</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" target="_blank">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="https://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>40%</td><td>10</td><td>4</td><td>6</td><td>0</td><td>4</td><td>6</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html" target="_blank">ReSEED: social event dEtection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>5</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html" target="_blank">A Multi-modal Graphical Model for Scene Analysis</a></td><td><a href="http://www.nicta.com.au/wp-content/uploads/2015/02/TaghaviNaminetalWACV15.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>8</td><td>4</td><td>4</td><td>0</td><td>5</td><td>3</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html" target="_blank">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="https://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></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>6</td><td>3</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html" target="_blank">Faces in Places: compound query retrieval</a></td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>1</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html" target="_blank">Spoofing faces using makeup: An investigative study</a></td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>France</td><td>43.61581310</td><td>7.06838000</td><td>60%</td><td>5</td><td>3</td><td>2</td><td>0</td><td>1</td><td>4</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html" target="_blank">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Mazeetal_IARPAJanusBenchmarkCFaceDatasetAndProtocol_ICB2018.pdf" target="_blank">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></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>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market1203</td><td>Market 1203</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</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>4</td><td>4</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</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>4</td><td>4</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html" target="_blank">Visual Kinship Recognition of Families in the Wild</a></td><td><a href="https://web.northeastern.edu/smilelab/fiw/papers/Supplemental_PP.pdf" target="_blank">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>University of Massachusetts Dartmouth</td><td>United States</td><td>41.62772475</td><td>-71.00724501</td><td>100%</td><td>3</td><td>3</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td><td>uccs</td><td>UCCS</td><td><a href="papers/d4f1eb008eb80595bcfdac368e23ae9754e1e745.html" target="_blank">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1708.02337.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</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>4</td><td>1</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html" target="_blank">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></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>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html" target="_blank">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>17%</td><td>12</td><td>2</td><td>10</td><td>1</td><td>11</td><td>1</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html" target="_blank">Investigating Open-World Person Re-identification Using a Drone</a></td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>2</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku</td><td>PKU</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html" target="_blank">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></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>1</td><td>2</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html" target="_blank">PETS 2017: Dataset and Challenge</a></td><td><a href="http://tahirnawaz.com/papers/2017_CVPRW_PETS2017Dataset_Luis_Nawaz_Cane_Ferryman.pdf" target="_blank">[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></td><td>12%</td><td>8</td><td>1</td><td>7</td><td>0</td><td>2</td><td>6</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html" target="_blank">MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</a></td><td><a href="https://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>3</td><td>1</td><td>2</td><td>0</td><td>2</td><td>1</td></tr><tr><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461.html" target="_blank">A 3 D Dynamic Database for Unconstrained Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>1</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html" target="_blank">Indian Face Age Database : A Database for Face Recognition with Age Variation</a></td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>2</td><td>0</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html" target="_blank">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>14%</td><td>7</td><td>1</td><td>6</td><td>0</td><td>7</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td>WLFDB</td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html" target="_blank">WLFDB: Weakly Labeled Face Databases</a></td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html" target="_blank">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><a href="https://scalable.mpi-inf.mpg.de/files/2016/01/main_wacv.pdf" target="_blank">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</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>2</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html" target="_blank">Re-identification of pedestrians with variable occlusion and scale</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>Kingston University</td><td>United Kingdom</td><td>51.42930860</td><td>-0.26840440</td><td>10%</td><td>10</td><td>1</td><td>9</td><td>2</td><td>6</td><td>4</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html" target="_blank">Crowdsourcing facial expressions for affective-interaction</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</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" target="_blank">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></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>e58dd160a76349d46f881bd6ddbc2921f08d1050</td><td>gfw</td><td>YouTube Pose</td><td><a href="papers/e58dd160a76349d46f881bd6ddbc2921f08d1050.html" target="_blank">Merge or Not? Learning to Group Faces via Imitation Learning</a></td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>2</td><td>0</td></tr><tr><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td><td>imdb_face</td><td>IMDB Face</td><td><a href="papers/9e31e77f9543ab42474ba4e9330676e18c242e72.html" target="_blank">The Devil of Face Recognition is in the Noise</a></td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Nanyang Technological University</td><td>Singapore</td><td>1.34841040</td><td>103.68297965</td><td>20%</td><td>5</td><td>1</td><td>4</td><td>0</td><td>3</td><td>1</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html" target="_blank">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>377f2b65e6a9300448bdccf678cde59449ecd337</td><td>ufdd</td><td>UFDD</td><td><a href="papers/377f2b65e6a9300448bdccf678cde59449ecd337.html" target="_blank">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></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>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html" target="_blank">Face swapping: automatically replacing faces in photographs</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</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>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html" target="_blank">FDDB: A benchmark for face detection in unconstrained settings</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</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>1</td></tr><tr><td>77c81c13a110a341c140995bedb98101b9e84f7f</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/77c81c13a110a341c140995bedb98101b9e84f7f.html" target="_blank">WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td>Unknown</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>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html" target="_blank">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td>CoRR</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>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/9696ad8b164f5e10fcfe23aacf74bd6168aebb15.html" target="_blank">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></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>2</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html" target="_blank">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Kentucky</td><td>United States</td><td>38.03337420</td><td>-84.50177580</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>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html" target="_blank">HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</a></td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></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>2</td></tr><tr><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/c866a2afc871910e3282fd9498dce4ab20f6a332.html" target="_blank">Surveillance Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td>CoRR</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>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html" target="_blank">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td>CoRR</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>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html" target="_blank">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</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>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4.html" target="_blank">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td>CoRR</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></table></body></html>
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