update uccs, terrance

1 files changed, 11 insertions, 7 deletions

diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html
index 5ec6380b..1674ae64 100644
--- a/site/public/datasets/uccs/index.html
+++ b/site/public/datasets/uccs/index.html
@@ -73,14 +73,17 @@
   </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><p>UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications"<a class="footnote_shim" name="[^uccs_vast]_1"> </a><a href="#[^uccs_vast]" class="footnote" title="Footnote 1">1</a>. According to the authors of <a href="https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745">two</a> <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1">papers</a> 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 3">3</a> In this investigation, we examine the contents of the <a href="http://vast.uccs.edu/Opensetface/">dataset</a>, its funding sources, photo EXIF data, and information from publicly available research project citations.</p>
-<p>The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain."<a class="footnote_shim" name="[^surv_face_qmul]_1"> </a><a href="#[^surv_face_qmul]" class="footnote" title="Footnote 4">4</a> The photos were taken during the spring semesters of 2012 &ndash; 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday."<a class="footnote_shim" name="[^sapkota_boult]_1"> </a><a href="#[^sapkota_boult]" class="footnote" title="Footnote 2">2</a>.</p>
+  </div></div><p>UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications"<a class="footnote_shim" name="[^uccs_vast]_1"> </a><a href="#[^uccs_vast]" class="footnote" title="Footnote 1">1</a>. According to the authors of <a href="https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745">two</a> <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1">papers</a> 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 3">3</a> This analysis examines the <a href="http://vast.uccs.edu/Opensetface/">UCCS dataset</a> contents of the <a href="">dataset</a>, its funding sources, timestamp data, and information from publicly available research project citations.</p>
+<p>The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. In 2018, it was the "largest surveillance [face recognition] benchmark in the public domain."<a class="footnote_shim" name="[^surv_face_qmul]_1"> </a><a href="#[^surv_face_qmul]" class="footnote" title="Footnote 4">4</a> The photos were taken during the spring semesters of 2012 &ndash; 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday."<a class="footnote_shim" name="[^sapkota_boult]_1"> </a><a href="#[^sapkota_boult]" class="footnote" title="Footnote 2">2</a>.</p>
 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_map_aerial.jpg' alt=' The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps'><div class='caption'> The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps</div></div></section><section><p>The long-range surveillance images in the UnConsrained College Students dataset were taken using a Canon 7D 18-megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured."<a class="footnote_shim" name="[^sapkota_boult]_2"> </a><a href="#[^sapkota_boult]" class="footnote" title="Footnote 2">2</a>
 Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face recognition systems for real world applications for defense, intelligence, and commercial partners.</p>
 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_grid.jpg' alt=' Example images from the UnConstrained College Students Dataset. Photos from UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt'><div class='caption'> Example images from the UnConstrained College Students Dataset. Photos from UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt</div></div></section><section><p>The EXIF data embedded in the images shows that the photo capture times follow a similar pattern to that outlined by the researchers, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken between Friday through Sunday shows that the researchers were only interested in capturing images of students during the peak campus hours.</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. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt'><div class='caption'> UCCS photos captured per weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt</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 weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License'><div class='caption'> UCCS photos captured per weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License</div></div></section><section><p>The two research 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>), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly <a href="https://vast.uccs.edu/project/iarpa-janus/">states</a> their involvement in the <a href="https://www.iarpa.gov/index.php/research-programs/janus">IARPA Janus</a> face recognition project developed to serve the needs of national intelligence, establishing that immediate benefactors of this dataset include United States defense and intelligence agencies, but it would go on to benefit other similar organizations.</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. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt'><div class='caption'> UCCS photos captured per weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License http://www.vast.uccs.edu/UCCS/License.txt</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 weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License'><div class='caption'> UCCS photos captured per weekday. Contains information from UCCS: UnConstrained College Students dataset, made available under a modified ODC Attribution License</div></div></section><section><p>The two research 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>), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly <a href="https://vast.uccs.edu/project/iarpa-janus/">states</a> their involvement in the <a href="https://www.iarpa.gov/index.php/research-programs/janus">IARPA Janus</a> face recognition project developed to serve the needs of national intelligence, establishing that benefactors of this dataset include United States defense and intelligence agencies, but it would go on to benefit other similar organizations.</p>
 <p>In 2017, one year after its public release, the UCCS face dataset formed the basis for a defense and intelligence agency funded <a href="http://www.face-recognition-challenge.com/">face recognition challenge</a> project 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.</p>
-<p>As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 4 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military; and Vision Semantics Ltd who lists the UK Ministry of Defence as a project partner.</p>
+<p>As of April 15, 2019, the UCCS dataset is no longer available. But during the time it was publicly available (2016 &ndash; 2019, based on publicly available resaearch citations) the UCCS dataset appeared in at least 4 research papers including usage from Beihang University who is known to provide research and development for China's military; and Vision Semantics Ltd who lists the UK Ministry of Defence as a project partner.</p>
+<h3>Updates</h3>
+<p>June 2, 2019: An email exchange with the author, Professor Terrance Boult, clarified that the he "did not provide data to any government agency when they collected it, nor does it appear that any US Government agency had ever downloaded it as part of the reserach competition." The funding was provided to assess the state of the art technology in face recognition.<a class="footnote_shim" name="[^boult_email]_1"> </a><a href="#[^boult_email]" class="footnote" title="Footnote 5">5</a></p>
+<p>However, this type of technology is data-driven and advancements are often derived in part from the dataset, as well as the author's own technical contributions.</p>
 </section><section>
   <h3>Who used UCCS?</h3>
 
@@ -142,7 +145,7 @@ Their setup made it impossible for students to know they were being photographed
   <h2>Supplementary Information</h2>
   
 </section><section><p>Since this site 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. &copy; megapixels.cc 2018. Based on the UnConstrained College Students dataset, made available under a modified ODC Attribution License'><div class='caption'> GAN generated approximations of students in the UCCS dataset. &copy; megapixels.cc 2018. Based on the UnConstrained College Students dataset, made available under a modified ODC Attribution License</div></div></section><section><div class='columns columns-2'><div class='column'><h4>UCCS photos taken in 2012</h4>
+</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. &copy; megapixels.cc 2018. Based on the UnConstrained College Students dataset, made available under a modified ODC Attribution License. Rendered using <a href="https://github.com/tkarras/progressive_growing_of_gans">Progressive Growing of GANs for Improved Quality, Stability, and Variation</a>'><div class='caption'> GAN generated approximations of students in the UCCS dataset. &copy; megapixels.cc 2018. Based on the UnConstrained College Students dataset, made available under a modified ODC Attribution License. Rendered using <a href="https://github.com/tkarras/progressive_growing_of_gans">Progressive Growing of GANs for Improved Quality, Stability, and Variation</a></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>
@@ -261,7 +264,7 @@ Their setup made it impossible for students to know they were being photographed
   <h4>Cite Our Work</h4>
   <p>
   	
-  	If you use our data, research, or graphics please cite our work:
+  	If you find this analysis helpful, please cite our work:
 
 <pre id="cite-bibtex">
 @online{megapixels,
@@ -276,7 +279,8 @@ Their setup made it impossible for students to know they were being photographed
 </section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^uccs_vast]" class="footnote_shim"></a><span class="backlinks"><a href="#[^uccs_vast]_1">a</a></span>"2nd Unconstrained Face Detection and Open Set Recognition Challenge." <a href="https://vast.uccs.edu/Opensetface/">https://vast.uccs.edu/Opensetface/</a>. Accessed April 15, 2019.
 </li><li>2 <a name="[^sapkota_boult]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sapkota_boult]_1">a</a><a href="#[^sapkota_boult]_2">b</a></span>Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
 </li><li>3 <a name="[^funding_uccs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^funding_uccs]_1">a</a></span>Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
-</li><li>4 <a name="[^surv_face_qmul]" class="footnote_shim"></a><span class="backlinks"><a href="#[^surv_face_qmul]_1">a</a></span>"Surveillance Face Recognition Challenge". <a href="https://www.semanticscholar.org/paper/Surveillance-Face-Recognition-Challenge-Cheng-Zhu/2306b2a8fba28539306052764a77a0d0f5d1236a">SemanticScholar</a>
+</li><li>4 <a name="[^surv_face_qmul]" class="footnote_shim"></a><span class="backlinks"><a href="#[^surv_face_qmul]_1">a</a></span>Cheng et. al. Surveillance Face Recognition Challenge. 2018. <a href="https://arxiv.org/abs/1804.09691">https://arxiv.org/abs/1804.09691</a>
+</li><li>5 <a name="[^boult_email]" class="footnote_shim"></a><span class="backlinks"><a href="#[^boult_email]_1">a</a></span>Email exchange on June 2, 2019.
 </li></ul></section></section>
 
   </div>