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<meta name="description" content="Unconstrained College Students (UCCS) is a dataset of long-range surveillance photos of students taken without their knowledge" />
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<section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'><span class="dataset-name">Unconstrained College Students (UCCS)</span> is a dataset of long-range surveillance photos of students taken without their knowledge</span></div><div class='hero_subdesc'><span class='bgpad'>The UCCS dataset includes 16,149 images and 1,732 identities of students at University of Colorado Colorado Springs campus and is used for face recognition and face detection
</span></div></div></section><section><div class='left-sidebar'><div class='meta'><div><div class='gray'>Published</div><div>2018</div></div><div><div class='gray'>Images</div><div>16,149</div></div><div><div class='gray'>Identities</div><div>1,732</div></div><div><div class='gray'>Used for</div><div>Face recognition, face detection</div></div><div><div class='gray'>Created by</div><div>Unviversity of Colorado Colorado Springs (US)</div></div><div><div class='gray'>Funded by</div><div>ODNI, IARPA, ONR MURI, Amry SBIR, SOCOM SBIR</div></div><div><div class='gray'>Website</div><div><a href="https://vast.uccs.edu/Opensetface/">vast.uccs.edu</a></div></div></div></div><h2>Unconstrained College Students ...</h2>
<p>(PAGE UNDER DEVELOPMENT)</p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_mean_bboxes_comp.jpg' alt=' The pixel-average of all Uconstrained College Students images is shown with all 51,838 face annotations. (c) Adam Harvey'><div class='caption'> The pixel-average of all Uconstrained College Students images is shown with all 51,838 face annotations. (c) Adam Harvey</div></div></section><section>
<h3>Biometric Trade Routes</h3>
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<p>
To help understand how UCCS has been used around the world for commercial, military and academic research; publicly available research citing UnConstrained College Students Dataset 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>
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<h3>Who used UCCS?</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.
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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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<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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Add [button/link] to download CSV. Add search input field to filter.
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</section><section><h3>Research Notes</h3>
<p>The original Sapkota and Boult dataset, from which UCCS is derived, received funding from<sup class="footnote-ref" id="fnref-funding_sb"><a href="#fn-funding_sb">1</a></sup>:</p>
<ul>
<li>ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638</li>
<li>Army SBIR (Small Business Innovation Research) grant W15P7T-12-C-A210</li>
<li>SOCOM (Special Operations Command) SBIR (Small Business Innovation Research) grant H92222-07-P-0020</li>
</ul>
<p>The more recent UCCS version of the dataset received funding from <sup class="footnote-ref" id="fnref-funding_uccs"><a href="#fn-funding_uccs">2</a></sup>:</p>
<ul>
<li>National Science Foundation Grant IIS-1320956</li>
<li>ODNI (Office of Director of National Intelligence)</li>
<li>IARPA (Intelligence Advance Research Projects Activity) R&D contract 2014-14071600012</li>
</ul>
<p>" In most face detection/recognition datasets, the majority of images are “posed”, i.e. the subjects know they are being photographed, and/or the images are selected for publication in public media. Hence, blurry, occluded and badly illuminated images are generally uncommon in these datasets. In addition, most of these challenges are close-set, i.e. the list of subjects in the gallery is the same as the one used for testing.</p>
<p>This challenge explores more unconstrained data, by introducing the new UnConstrained College Students (UCCS) dataset, where subjects are photographed using a long-range high-resolution surveillance camera without their knowledge. Faces inside these images are of various poses, and varied levels of blurriness and occlusion. The challenge also creates an open set recognition problem, where unknown people will be seen during testing and must be rejected.</p>
<p>With this challenge, we hope to foster face detection and recognition research towards surveillance applications that are becoming more popular and more required nowadays, and where no automatic recognition algorithm has proven to be useful yet.</p>
<p>UnConstrained College Students (UCCS) Dataset</p>
<p>The UCCS dataset was collected over several months using Canon 7D camera fitted with Sigma 800mm F5.6 EX APO DG HSM lens, taking images at one frame per second, during times when many students were walking on the sidewalk. "</p>
<div class="footnotes">
<hr>
<ol><li id="fn-funding_sb"><p>Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.<a href="#fnref-funding_sb" class="footnote">↩</a></p></li>
<li id="fn-funding_uccs"><p>Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.<a href="#fnref-funding_uccs" class="footnote">↩</a></p></li>
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