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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</span> is a dataset of long-range surveillance photos of students on University of Colorado in Colorado Springs campus</span></div><div class='hero_subdesc'><span class='bgpad'>The UnConstrained College Students dataset includes 16,149 images of 1,732 students, faculty, and pedestrians and is used for developing face recognition and face detection algorithms
</span></div></div></section><section><h2>UnConstrained College Students</h2>
</section><div class='right-sidebar'><div class='meta'>
<div class='gray'>Published</div>
<div>2016</div>
</div><div class='meta'>
<div class='gray'>Images</div>
<div>16,149 </div>
</div><div class='meta'>
<div class='gray'>Identities</div>
<div>1,732 </div>
</div><div class='meta'>
<div class='gray'>Purpose</div>
<div>Face recognition, face detection</div>
</div><div class='meta'>
<div class='gray'>Created by</div>
<div>University of Colorado Colorado Springs (US)</div>
</div><div class='meta'>
<div class='gray'>Funded by</div>
<div>ODNI, IARPA, ONR MURI, Amry SBIR, SOCOM SBIR</div>
</div><div class='meta'>
<div class='gray'>Website</div>
<div><a href='http://vast.uccs.edu/Opensetface/' target='_blank' rel='nofollow noopener'>uccs.edu</a></div>
</div></div><section><p>UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge" <a class="footnote_shim" name="[^funding_uccs]_1"> </a><a href="#[^funding_uccs]" class="footnote" title="Footnote 2">2</a>. In this investigation, we examine the funding sources, contents of the dataset, photo EXIF data, and publicy available research project citations.</p>
<p>According to the author's of the the UnConstrained College Students dataset it is primarliy used for research and development of "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." Applications of this technology include usage by defense and intelligence agencies, who were also the primary funding sources of the UCCS dataset.</p>
<p>In the two papers associated with the release of the UCCS dataset (<a href="https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a> and <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1">Large Scale Unconstrained Open Set Face Database</a>), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity <a href="https://vast.uccs.edu/project/iarpa-janus/">states</a> they are part of the <a href="https://www.iarpa.gov/index.php/research-programs/janus">IARPA Janus</a>, a face recognition project developed to serve the needs of national intelligence interests.</p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_map_aerial.jpg' alt=' Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps'><div class='caption'> Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps</div></div></section><section><p>The UCCS dataset includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP) and was, as of 2018, the "largest surveillance FR benchmark in the public domain."<a class="footnote_shim" name="[^surv_face_qmul]_1"> </a><a href="#[^surv_face_qmul]" class="footnote" title="Footnote 3">3</a> To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students from a distance of 150–200m through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. According to an analysis of the EXIF data embedded in the photos, nearly half of the 16,149 photos were taken on Tuesdays. The most popular time was during lunch break. All of the photos were taken during the spring semester in 2012 and 2013 but the dataset was not publicy released until 2016.</p>
<p>In 2017 the UCCS face dataset was used for a defense and intelligence agency funded <a href="http://www.face-recognition-challenge.com/">face recognition challenge</a> at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the <a href="https://erodner.github.io/ial2018eccv/">2nd Unconstrained Face Detection and Open Set Recognition Challenge</a> at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations.</p>
<p>As of April 15, 2019, the UCCS dataset is no longer available for public download. During the three years it was publicly available (2016-2019) the UCCS dataset apepared in at least 5 publicly available research papers including verified usage from University of Notre Dame (US), Beihang University (China), Beckman Institute (US), Queen Mary University of London (UK), Carnegie Mellon University (US),Karlsruhe Institute of Technology (DE), and Vision Semantics Ltd (UK) who <a href="http://visionsemantics.com/partners.html">lists</a> the UK Ministry of Defence and Metropolitan Police as partners.</p>
<p>To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.</p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_pgan_01.jpg' alt=' GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018'><div class='caption'> GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018</div></div></section><section>
<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.
</p>
</section>
<section class="applet_container">
<!-- <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>
<h3>Biometric Trade Routes</h3>
<p>
To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location.
</p>
</section>
<section class="applet_container fullwidth">
<div class="applet" data-payload="{"command": "map"}"></div>
</section>
<div class="caption">
<ul class="map-legend">
<li class="edu">Academic</li>
<li class="com">Commercial</li>
<li class="gov">Military / Government</li>
</ul>
<div class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</div >
</div>
<section class="applet_container">
<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.
</p>
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</section><section>
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<div class="hr-wave-line hr-wave-line2"></div>
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<h2>Supplementary Information</h2>
</section><section><h3>Dates and Times</h3>
<p>The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the <a href="assets/uccs_camera_exif.csv">EXIF data</a> embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM.</p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_exif_plot_days.png' alt=' UCCS photos captured per weekday © megapixels.cc'><div class='caption'> UCCS photos captured per weekday © megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_exif_plot.png' alt=' UCCS photos captured per 10-minute intervals per weekday © megapixels.cc'><div class='caption'> UCCS photos captured per 10-minute intervals per weekday © megapixels.cc</div></div></section><section><div class='columns columns-2'><div class='column'><h4>UCCS photos taken in 2012</h4>
<table>
<thead><tr>
<th>Date</th>
<th>Photos</th>
</tr>
</thead>
<tbody>
<tr>
<td>Feb 23, 2012</td>
<td>132</td>
</tr>
<tr>
<td>March 6, 2012</td>
<td>288</td>
</tr>
<tr>
<td>March 8, 2012</td>
<td>506</td>
</tr>
<tr>
<td>March 13, 2012</td>
<td>160</td>
</tr>
<tr>
<td>March 20, 2012</td>
<td>1,840</td>
</tr>
<tr>
<td>March 22, 2012</td>
<td>445</td>
</tr>
<tr>
<td>April 3, 2012</td>
<td>1,639</td>
</tr>
<tr>
<td>April 12, 2012</td>
<td>14</td>
</tr>
<tr>
<td>April 17, 2012</td>
<td>19</td>
</tr>
<tr>
<td>April 24, 2012</td>
<td>63</td>
</tr>
<tr>
<td>April 25, 2012</td>
<td>11</td>
</tr>
<tr>
<td>April 26, 2012</td>
<td>20</td>
</tr>
</tbody>
</table>
</div><div class='column'><h4>UCCS photos taken in 2013</h4>
<table>
<thead><tr>
<th>Date</th>
<th>Photos</th>
</tr>
</thead>
<tbody>
<tr>
<td>Jan 28, 2013</td>
<td>1,056</td>
</tr>
<tr>
<td>Jan 29, 2013</td>
<td>1,561</td>
</tr>
<tr>
<td>Feb 13, 2013</td>
<td>739</td>
</tr>
<tr>
<td>Feb 19, 2013</td>
<td>723</td>
</tr>
<tr>
<td>Feb 20, 2013</td>
<td>965</td>
</tr>
<tr>
<td>Feb 26, 2013</td>
<td>736</td>
</tr>
</tbody>
</table>
</div></div></section><section><h3>Location</h3>
<p>The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1">original papers</a> also provides another clue: a <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1/figure/1">picture of the camera</a> inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the <a href="https://www.google.com/maps/place/University+of+Colorado+Colorado+Springs/@38.8934297,-104.7992445,27a,35y,258.51h,75.06t/data=!3m1!1e3!4m5!3m4!1s0x87134fa088fe399d:0x92cadf3962c058c4!8m2!3d38.8968312!4d-104.8049528">location on Google Maps</a></p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_map_3d.jpg' alt=' 3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps'><div class='caption'> 3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps</div></div></section><section><h3>Funding</h3>
<p>The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:</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>
<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>
<h3>Opting Out</h3>
<p>If you attended University of Colorado Colorado Springs and were captured by the long range surveillance camera used to create this dataset, there is unfortunately currently no way to be removed. The authors do not provide any options for students to opt-out nor were students informed they would be used for training face recognition. According to the authors, the lack of any consent or knowledge of participation is what provides part of the value of Unconstrained College Students Dataset.</p>
<h3>Ethics</h3>
<ul>
<li>Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs <a href="https://www.uccs.edu/compliance/">Ethics and Compliance Office</a></li>
<li>For further technical information about the dataset, visit the <a href="https://vast.uccs.edu/Opensetface">UCCS dataset project page</a>. </li>
</ul>
<h3>Downloads</h3>
<ul>
<li>Download EXIF data for UCCS photos: <a href="https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_camera_exif.csv">uccs_camera_exif.csv</a></li>
</ul>
</section><section>
<h4>Cite Our Work</h4>
<p>
If you use our data, research, or graphics please cite our work:
<pre id="cite-bibtex">
@online{megapixels,
author = {Harvey, Adam. LaPlace, Jules.},
title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
year = 2019,
url = {https://megapixels.cc/},
urldate = {2019-04-20}
}</pre>
</p>
</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^funding_sb]" class="footnote_shim"></a><span class="backlinks"></span><p>Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.</p>
</li><li><a name="[^funding_uccs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^funding_uccs]_1">a</a></span><p>Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.</p>
</li><li><a name="[^surv_face_qmul]" class="footnote_shim"></a><span class="backlinks"><a href="#[^surv_face_qmul]_1">a</a></span><p>"Surveillance Face Recognition Challenge". <a href="https://www.semanticscholar.org/paper/Surveillance-Face-Recognition-Challenge-Cheng-Zhu/2306b2a8fba28539306052764a77a0d0f5d1236a">SemanticScholar</a></p>
</li></ul></section></section>
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