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<meta name="author" content="Adam Harvey" />
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<div class='site_name'>MegaPixels</div>
<div class='splash'>COFW</div>
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<a href="/about/">About</a>
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<section><div class='left-sidebar'><div class='meta'>
<div class='gray'>Published</div>
<div>2013</div>
</div><div class='meta'>
<div class='gray'>Images</div>
<div>1,007 </div>
</div><div class='meta'>
<div class='gray'>Purpose</div>
<div>challenging dataset (sunglasses, hats, interaction with objects)</div>
</div><div class='meta'>
<div class='gray'>Website</div>
<div><a href='http://www.vision.caltech.edu/xpburgos/ICCV13/' target='_blank' rel='nofollow noopener'>caltech.edu</a></div>
</div><div class='meta'><div><div class='gray'>Years</div><div>1993-1996</div></div><div><div class='gray'>Images</div><div>14,126</div></div><div><div class='gray'>Identities</div><div>1,199 </div></div><div><div class='gray'>Origin</div><div>Web Searches</div></div><div><div class='gray'>Funded by</div><div>ODNI, IARPA, Microsoft</div></div></div></div><h2>Caltech Occluded Faces in the Wild</h2>
<p>(PAGE UNDER DEVELOPMENT)</p>
<p>COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion].</p>
<p>RESEARCH below this line</p>
<blockquote><p>We asked four people with different levels of computer vision knowledge to each collect 250 faces representative of typical real-world images, with the clear goal of challenging computer vision methods.
The result is 1,007 images of faces obtained from a variety of sources.</p>
</blockquote>
<p>Robust face landmark estimation under occlusion</p>
<blockquote><p>Our face dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food, hands, microphones, etc.). All images were hand annotated in our lab using the same 29 landmarks as in LFPW. We annotated both the landmark positions as well as their occluded/unoccluded state. The faces are occluded to different degrees, with large variations in the type of occlusions encountered. COFW has an average occlusion of over 23%.
To increase the number of training images, and since COFW has the exact same landmarks as LFPW, for training we use the original non-augmented 845 LFPW faces + 500 COFW faces (1345 total), and for testing the remaining 507 COFW faces. To make sure all images had occlusion labels, we annotated occlusion on the available 845 LFPW training images, finding an average of only 2% occlusion.</p>
</blockquote>
<p><a href="http://www.vision.caltech.edu/xpburgos/ICCV13/">http://www.vision.caltech.edu/xpburgos/ICCV13/</a></p>
<blockquote><p>This research is supported by NSF Grant 0954083 and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012.</p>
</blockquote>
<p><a href="https://www.cs.cmu.edu/~peiyunh/topdown/">https://www.cs.cmu.edu/~peiyunh/topdown/</a></p>
</section><section>
<h3>Biometric Trade Routes</h3>
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<p>
To help understand how COFW Dataset has been used around the world for commercial, military and academic research; publicly available research citing Caltech Occluded Faces in the Wild is collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location.
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<li class="edu">Academic</li>
<li class="com">Commercial</li>
<li class="gov">Military / Government</li>
<li class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</li>
</ul>
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[section under development] COFW Dataset ... Standardized paragraph of text about the map. Sed ut perspiciatis, unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo.
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<h3>Supplementary Information</h3>
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<h3>Dataset Citations</h3>
<p>
The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms.
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<h3>Who used COFW Dataset?</h3>
<p>
This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries.
</p>
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<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span>
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</section><section><p>TODO</p>
<h2>- replace graphic</h2>
</section>
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