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@@ -20,38 +20,41 @@ authors: Adam Harvey
 
 ## UnConstrained College Students
 
-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" [^funding_uccs]. In this investigation, we examine the funding sources, contents of the dataset, photo EXIF data, and publicy available research project citations.
+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"[^uccs_vast]. 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".[^funding_uccs] In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.
 
-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.
+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."[^surv_face_qmul] The photos were taken during the spring semesters of 2012 – 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."[^sapkota_boult]. 
 
+![caption: Example images from the UnConstrained College Students Dataset. ](assets/uccs_grid.jpg)
 
+The long-range surveillance images in the UnContsrained College Students dataset were captured 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."[^sapkota_boult]
+Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications.
 
-In the two papers associated with the release of the UCCS datasaet ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), 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 [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests.
+![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](assets/uccs_map_aerial.jpg)
 
-![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](assets/uccs_map_aerial.jpg)
+The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, 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 on Friday shows that the researchers were only interested in capturing images of students.
 
-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."[^surv_face_qmul] 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.
+![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot_days.png)
 
-In 2017 the UCCS face dataset was used for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) 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.
+![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot.png)
 
-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 [lists](http://visionsemantics.com/partners.html) the UK Ministry of Defence and Metropolitan Police as partners.
+The two research papers associated with the release of the UCCS dataset ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed 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), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
+
+
+Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. 
+
+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 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military.
 
-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.
 
-![caption: GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018](assets/uccs_pgan_01.jpg)
 
 
 {% include 'dashboard.html' %}
 
 {% include 'supplementary_header.html' %}
 
-### Dates and Times
-
-The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the [EXIF data](assets/uccs_camera_exif.csv) 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.
 
-![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot_days.png)
+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.
 
-![caption: UCCS photos captured per 10-minute intervals per weekday © megapixels.cc](assets/uccs_exif_plot.png)
+![caption: GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018](assets/uccs_pgan_01.jpg)
 
 
 === columns 2
@@ -114,7 +117,7 @@ If you attended University of Colorado Colorado Springs and were captured by the
 ### Ethics
 
 - Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs [Ethics and Compliance Office](https://www.uccs.edu/compliance/)
-- For further technical information about the dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). 
+- For further technical information about the UnConstrained College Students dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). 
 
 ### Downloads
 
@@ -126,6 +129,7 @@ If you attended University of Colorado Colorado Springs and were captured by the
 
 ### Footnotes
 
-[^funding_sb]: Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
+[^uccs_vast]: "2nd Unconstrained Face Detection and Open Set Recognition Challenge." <https://vast.uccs.edu/Opensetface/>. Accessed April 15, 2019.
+[^sapkota_boult]: Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
 [^funding_uccs]: Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
 [^surv_face_qmul]: "Surveillance Face Recognition Challenge". [SemanticScholar](https://www.semanticscholar.org/paper/Surveillance-Face-Recognition-Challenge-Cheng-Zhu/2306b2a8fba28539306052764a77a0d0f5d1236a)