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diff --git a/site/content/pages/about/credits.md b/site/content/pages/about/credits.md deleted file mode 100644 index bc2283fd..00000000 --- a/site/content/pages/about/credits.md +++ /dev/null @@ -1,48 +0,0 @@ ------------- - -status: published -title: MegaPixels Press and News -desc: MegaPixels Press and News -slug: press -cssclass: about -published: 2018-12-04 -updated: 2018-12-04 -authors: Adam Harvey - ------------- - -# Credits - -<section class="about-menu"> -<ul> - <li><a href="/about/">About</a></li> - <li><a href="/about/press/">Press</a></li> - <li><a class="current" href="/about/credits/">Credits</a></li> - <li><a href="/about/disclaimer/">Disclaimer</a></li> - <li><a href="/about/terms/">Terms and Conditions</a></li> - <li><a href="/about/privacy/">Privacy Policy</a></li> -</ul> -</section> - - -#### Team - -- Research and image analysis: Adam Harvey -- Development and visualizations: Jules LaPlace -- Produced in Partnership with Mozilla -- Contributing researchers: Berit Gilma, Mathana Stender - -#### Code - -<<<<<<< HEAD -- This site uses D3.js, C3.js, and ThreeJS for visualizations. -- Data aggregation uses Pandas and PDFMiner.Six. -======= -- This site uses D3 and C2 for visuzations -- Add more here ->>>>>>> 26646e6adf3833f6282e9515c14ad61e485440c0 - -#### Data - -- link to github -- how it was gathered
\ No newline at end of file diff --git a/site/content/pages/about/disclaimer.md b/site/content/pages/about/disclaimer.md deleted file mode 100644 index f82a09a0..00000000 --- a/site/content/pages/about/disclaimer.md +++ /dev/null @@ -1,41 +0,0 @@ ------------- - -status: published -title: Disclaimer -desc: MegaPixels Disclaimer -slug: disclaimer -cssclass: about -published: 2018-12-04 -updated: 2018-12-04 -authors: Adam Harvey - ------------- - -# Disclaimer - -<section class="about-menu"> -<ul> -<li><a href="/about/">About</a></li> -<li><a href="/about/press/">Press</a></li> -<li><a href="/about/credits/">Credits</a></li> -<li><a class="current" href="/about/disclaimer/">Disclaimer</a></li> -<li><a href="/about/terms/">Terms and Conditions</a></li> -<li><a href="/about/privacy/">Privacy Policy</a></li> -</ul> -</section> - -(TEMPORARY PAGE) - -Last updated: December 04, 2018 - -The information contained on MegaPixels.cc website (the "Service") is for academic and artistic purposes only. - -MegaPixels.cc assumes no responsibility for errors or omissions in the contents on the Service. - -In no event shall MegaPixels.cc be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. MegaPixels.cc reserves the right to make additions, deletions, or modification to the contents on the Service at any time without prior notice. - -## External links disclaimer - -MegaPixels.cc website may contain links to external websites that are not provided or maintained by or in any way affiliated with MegaPixels.cc - -Please note that the MegaPixels.cc does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites. diff --git a/site/content/pages/about/faq.md b/site/content/pages/about/faq.md new file mode 100644 index 00000000..8b780262 --- /dev/null +++ b/site/content/pages/about/faq.md @@ -0,0 +1,26 @@ +------------ + +status: published +title: MegaPixels Press and News +desc: MegaPixels Press and News +slug: faqs +cssclass: about +published: 2018-12-04 +updated: 2018-12-04 +authors: Adam Harvey + +------------ + +# FAQs + +<section class="about-menu"> +<ul> +<li><a href="/about/">About</a></li> +<li><a class="current" href="/about/faq/">FAQs</a></li> +<li><a href="/about/press/">Press</a></li> +<li><a href="/about/terms/">Terms</a></li> +<li><a href="/about/privacy/">Privacy</a></li> +</ul> +</section> + +[ page under development ] diff --git a/site/content/pages/about/index.md b/site/content/pages/about/index.md index 4fec0777..5fc147c9 100644 --- a/site/content/pages/about/index.md +++ b/site/content/pages/about/index.md @@ -16,41 +16,63 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a class="current" href="/about/">About</a></li> +<li><a href="/about/faq/">FAQs</a></li> <li><a href="/about/press/">Press</a></li> -<li><a href="/about/credits/">Credits</a></li> -<li><a href="/about/disclaimer/">Disclaimer</a></li> -<li><a href="/about/terms/">Terms and Conditions</a></li> -<li><a href="/about/privacy/">Privacy Policy</a></li> +<li><a href="/about/terms/">Terms</a></li> +<li><a href="/about/privacy/">Privacy</a></li> </ul> </section> -(PAGE UNDER DEVELOPMENT) +MegaPixels is an independent art and research project by Adam Harvey and Jules LaPlace investigating the ethics and individual privacy implications of publicly available face recognition datasets, and their role in industry and governmental expansion into biometric surveillance technologies. -<p><div style="font-size:20px;line-height:36px">Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to technological advancements. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance cameras on college campuses, search engine queries for celebrities, cafe livestreams, and <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">personal videos</a> posted on YouTube. </div></p> - -Collectively, facial recognition datasets are now gathered "in the wild". - -<p>MegaPixels is art and research by <a href="https://ahprojects.com">Adam Harvey</a> about facial recognition datasets that unravels their histories, futures, geographies, and meanings. Throughout 2019 this site this site will publish research reports, visualizations, raw data, and interactive tools to explore how publicly available facial recognition datasets contribute to a global supply chain of biometric data that powers the global facial recognition industry.</p> - -During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is powering the global facial recognition industry. - -The MegaPixels website is produced in partnership with [Mozilla](https://mozilla.org). +The MegaPixels site is made possible with support from <a href="http://mozilla.org">Mozilla</a> <div class="flex-container team-photos-container"> <div class="team-member"> - <img src="https://nyc3.digitaloceanspaces.com/megapixels/v1/site/about/assets/adam-harvey-3d.jpg" /> <h3>Adam Harvey</h3> - <p>is Berlin-based American artist and researcher. His previous projects (CV Dazzle, Stealth Wear, and SkyLift) explore the potential for countersurveillance as artwork. He is the founder of VFRAME (visual forensics software for human rights groups), the recipient of 2 PrototypeFund grants, and is a researcher in residence at Karlsruhe HfG. - <br> - <a href="https://ahprojects.com">ahprojects.com</a> + <p>is Berlin-based American artist and researcher. His previous projects (CV Dazzle, Stealth Wear, and SkyLift) explore the potential for counter-surveillance as artwork. He is the founder of VFRAME (visual forensics software for human rights groups) and is a currently researcher in residence at Karlsruhe HfG.</p> + <p><a href="https://ahprojects.com">ahprojects.com</a></p> </p> </div> <div class="team-member"> - <img src="https://nyc3.digitaloceanspaces.com/megapixels/v1/site/about/assets/jules-laplace-3d.jpg" /> <h3>Jules LaPlace</h3> - <p>is an American creative technologist also based in Berlin. He was previously the CTO of a digital agency in NYC and now also works at VFRAME, developing computer vision for human rights groups. Jules also builds creative software for artists and musicians. - <br> - <a href="https://asdf.us/">asdf.us</a> + <p>is an American technologist and artist also based in Berlin. He was previously the CTO of a digital agency in NYC and now also works at VFRAME, developing computer vision and data analysis software for human rights groups. Jules also builds experimental software for artists and musicians. </p> + <p><a href="https://asdf.us/">asdf.us</a></p> </div> </div> + +MegaPixels.cc is an independent research project about publicly available face recognition datasets. This website is based, in part, on earlier installations and research projects about facial recognition datasets in 2016-2018, which focused particularly on the MegaFace dataset. Since then it has evolved into a large-scale survey of publicly-available face and person analysis datasets, covering their usage, geographies, and ethics. + +An academic report and presentation on the findings is forthcoming. This site is published to make the research more accessible to a wider audience and to include visualizations and interactive features not possible in PDF publications. Continued research on MegaPixels is supported by a 1 year Researcher-in-Residence grant from Karlsruhe HfG. + +When possible, and once thoroughly verified, data generated for MegaPixels will be made available for download on [github.com/adamhrv/megapixels](https://github.com/adamhrv/megapixels) + +=== columns 3 + +#### Team + +- Adam Harvey: Concept, research, design, computer vision +- Jules LaPlace: Information and systems architecture, data retrieval, web applications + +=========== + +#### Contributing Researchers + +- Berit Gilma: Dataset statistics and discovery +- Beth (aka Ms. Celeb): Dataset usage verification and research +- Mathana Stender: Commercial usage verification and research on LFW + +=========== + +#### Code and Libraries + +- [Semantic Scholar](https://semanticscholar.org) for citation aggregation +- Leaflet.js for maps +- C3.js for charts +- ThreeJS for 3D visualizations +- PDFMiner.Six and Pandas for research paper data analysis + +=== end columns + +Please direct questions, comments, or feedback to [mastodon.social/@adamhrv](https://mastodon.social/@adamhrv)
\ No newline at end of file diff --git a/site/content/pages/about/press.md b/site/content/pages/about/press.md index 47e1af52..a0780d64 100644 --- a/site/content/pages/about/press.md +++ b/site/content/pages/about/press.md @@ -16,11 +16,10 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/faq/">FAQs</a></li> <li><a class="current" href="/about/press/">Press</a></li> -<li><a href="/about/credits/">Credits</a></li> -<li><a href="/about/disclaimer/">Disclaimer</a></li> -<li><a href="/about/terms/">Terms and Conditions</a></li> -<li><a href="/about/privacy/">Privacy Policy</a></li> +<li><a href="/about/terms/">Terms</a></li> +<li><a href="/about/privacy/">Privacy</a></li> </ul> </section> diff --git a/site/content/pages/about/privacy.md b/site/content/pages/about/privacy.md index e36daf2a..48598623 100644 --- a/site/content/pages/about/privacy.md +++ b/site/content/pages/about/privacy.md @@ -16,11 +16,10 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/faq/">FAQs</a></li> <li><a href="/about/press/">Press</a></li> -<li><a href="/about/credits/">Credits</a></li> -<li><a href="/about/disclaimer/">Disclaimer</a></li> -<li><a href="/about/terms/">Terms and Conditions</a></li> -<li><a class="current" href="/about/privacy/">Privacy Policy</a></li> +<li><a href="/about/terms/">Terms</a></li> +<li><a class="current" href="/about/privacy/">Privacy</a></li> </ul> </section> diff --git a/site/content/pages/about/terms.md b/site/content/pages/about/terms.md index 7ae6dac7..e03bafaa 100644 --- a/site/content/pages/about/terms.md +++ b/site/content/pages/about/terms.md @@ -17,11 +17,10 @@ authors: Adam Harvey <section class="about-menu"> <ul> <li><a href="/about/">About</a></li> +<li><a href="/about/faq/">FAQs</a></li> <li><a href="/about/press/">Press</a></li> -<li><a href="/about/credits/">Credits</a></li> -<li><a href="/about/disclaimer/">Disclaimer</a></li> -<li><a class="current" href="/about/terms/">Terms and Conditions</a></li> -<li><a href="/about/privacy/">Privacy Policy</a></li> +<li><a class="current" href="/about/terms/">Terms</a></li> +<li><a href="/about/privacy/">Privacy</a></li> </ul> </section> diff --git a/site/content/pages/datasets/brainwash/assets/00818000_640x480.jpg b/site/content/pages/datasets/brainwash/assets/00818000_640x480.jpg Binary files differdeleted file mode 100644 index 30c0fcb1..00000000 --- a/site/content/pages/datasets/brainwash/assets/00818000_640x480.jpg +++ /dev/null diff --git a/site/content/pages/datasets/brainwash/assets/background_540.jpg b/site/content/pages/datasets/brainwash/assets/background_540.jpg Binary files differdeleted file mode 100644 index 5c8c0ad4..00000000 --- a/site/content/pages/datasets/brainwash/assets/background_540.jpg +++ /dev/null diff --git a/site/content/pages/datasets/brainwash/assets/background_600.jpg 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--- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -2,8 +2,8 @@ status: published title: Brainwash -desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco -subdesc: The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection algorithms +desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014 +subdesc: The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms slug: brainwash cssclass: dataset image: assets/background.jpg @@ -19,28 +19,23 @@ authors: Adam Harvey + Published: 2015 + Images: 11,918 + Faces: 91,146 -+ Created by: Stanford Department of Computer Science ++ Created by: Stanford University (US)<br>Max Planck Institute for Informatics (DE) + Funded by: Max Planck Center for Visual Computing and Communication -+ Location: Brainwash Cafe, San Franscisco -+ Purpose: Training face detection ++ Purpose: Head detection ++ Download Size: 4.1GB + Website: <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">stanford.edu</a> -+ Paper: <a href="http://arxiv.org/abs/1506.04878">End-to-End People Detection in Crowded Scenes</a> -+ Explicit Consent: No ## Brainwash Dataset -(PAGE UNDER DEVELOPMENT) +*Brainwash* is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com [cite orig paper]. -*Brainwash* is a face detection dataset created from the Brainwash Cafe's livecam footage including 11,918 images of "everyday life of a busy downtown cafe[^readme]". The images are used to develop face detection algorithms for the "challenging task of detecting people in crowded scenes" and tracking them. +Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance [^localized_region_context] [^replacement_algorithm]. -Before closing in 2017, Brainwash Cafe was a "cafe and laundromat" located in San Francisco's SoMA district. The cafe published a publicy available livestream from the cafe with a view of the cash register, performance stage, and seating area. +If you happen to have been at Brainwash cafe in San Franscisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset. -Since it's publication by Stanford in 2015, the Brainwash dataset has appeared in several notable research papers. In September 2016 four researchers from the National University of Defense Technology in Changsha, China used the Brainwash dataset for a research study on "people head detection in crowded scenes", concluding that their algorithm "achieves superior head detection performance on the crowded scenes dataset[^localized_region_context]". And again in 2017 three researchers at the National University of Defense Technology used Brainwash for a study on object detection noting "the data set used in our experiment is shown in Table 1, which includes one scene of the brainwash dataset[^replacement_algorithm]". + - - - {% include 'chart.html' %} @@ -48,19 +43,27 @@ Since it's publication by Stanford in 2015, the Brainwash dataset has appeared i {% include 'map.html' %} -Add more analysis here - +{% include 'citations.html' %} {% include 'supplementary_header.html' %} -{% include 'citations.html' %} + + -### Additional Information +#### Additional Resources - The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> +TODO + +- add bounding boxes to the header image +- remake montage with randomized images, with bboxes +- clean up intro text +- verify quote citations + + ### Footnotes [^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. diff --git a/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cam5_average_comp.jpg b/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cam5_average_comp.jpg Binary files differnew file mode 100755 index 00000000..3cd64df1 --- /dev/null +++ b/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cam5_average_comp.jpg diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index de1fa14c..c626ef4e 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -2,8 +2,8 @@ status: published title: Duke Multi-Target, Multi-Camera Tracking -desc: <span class="dataset-name">Duke MTMC</span> is a dataset of CCTV footage of students at Duke University -subdesc: Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 cameras at Duke University campus in March 2014 +desc: <span class="dataset-name">Duke MTMC</span> is a dataset of surveillance camera footage of students on Duke University campus +subdesc: Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 HD cameras at Duke University campus in March 2014 slug: duke_mtmc cssclass: dataset image: assets/background.jpg @@ -15,17 +15,27 @@ authors: Adam Harvey ### sidebar -+ Collected: March 19, 2014 -+ Cameras: 8 -+ Video Frames: 2,000,000 -+ Identities: Over 2,000 -+ Used for: Person re-identification, <br>face recognition -+ Sector: Academic ++ Created: 2014 ++ Identities: Over 2,700 ++ Used for: Face recognition, person re-identification ++ Created by: Computer Science Department, Duke University, Durham, US + Website: <a href="http://vision.cs.duke.edu/DukeMTMC/">duke.edu</a> ## Duke Multi-Target, Multi-Camera Tracking Dataset (Duke MTMC) -(PAGE UNDER DEVELOPMENT) +[ PAGE UNDER DEVELOPMENT ] + +Duke MTMC is a dataset of video recorded on Duke University campus during for the purpose of training, evaluating, and improving *multi-target multi-camera tracking*. The videos were recorded during February and March 2014 and cinclude + +Includes a total of 888.8 minutes of video (ind. verified) + +"We make available a new data set that has more than 2 million frames and more than 2,700 identities. It consists of 8×85 minutes of 1080p video recorded at 60 frames per second from 8 static cameras deployed on the Duke University campus during periods between lectures, when pedestrian traffic is heavy." + +The dataset includes approximately 2,000 annotated identities appearing in 85 hours of video from 8 cameras located throughout Duke University's campus. + + + +According to the dataset authors, {% include 'map.html' %} diff --git a/site/content/pages/datasets/index.md b/site/content/pages/datasets/index.md index 2e943fbe..c0373d60 100644 --- a/site/content/pages/datasets/index.md +++ b/site/content/pages/datasets/index.md @@ -13,4 +13,4 @@ sync: false # Facial Recognition Datasets -### Survey +Explore publicly available facial recognition datasets. More datasets will be added throughout 2019. diff --git a/site/content/pages/datasets/msceleb/assets/background.jpg b/site/content/pages/datasets/msceleb/assets/background.jpg Binary files differnew file mode 100644 index 00000000..c1cd486e --- /dev/null +++ b/site/content/pages/datasets/msceleb/assets/background.jpg diff --git a/site/content/pages/datasets/msceleb/assets/index.jpg b/site/content/pages/datasets/msceleb/assets/index.jpg Binary files differnew file mode 100644 index 00000000..fb3a934a --- /dev/null +++ b/site/content/pages/datasets/msceleb/assets/index.jpg diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md new file mode 100644 index 00000000..eb084eaa --- /dev/null +++ b/site/content/pages/datasets/msceleb/index.md @@ -0,0 +1,56 @@ +------------ + +status: published +title: MS Celeb +desc: MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms +subdesc: The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine +slug: msceleb +cssclass: dataset +image: assets/background.jpg +year: 2015 +published: 2019-2-23 +updated: 2019-2-23 +authors: Adam Harvey + +------------ + +### sidebar + ++ Published: TBD ++ Images: TBD ++ Faces: TBD ++ Created by: TBD + + +## Microsoft Celeb Dataset (MS Celeb) + +(PAGE UNDER DEVELOPMENT) + +At vero eos et accusamus et iusto odio dignissimos ducimus, qui blanditiis praesentium voluptatum deleniti atque corrupti, quos dolores et quas molestias excepturi sint, obcaecati cupiditate non-provident, similique sunt in culpa, qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio. + +Nam libero tempore, cum soluta nobis est eligendi optio, cumque nihil impedit, quo minus id, quod maxime placeat, facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Temporibus autem quibusdam et aut officiis debitis aut rerum necessitatibus saepe eveniet, ut et voluptates repudiandae sint et molestiae non-recusandae. Itaque earum rerum hic tenetur a sapiente delectus, ut aut reiciendis voluptatibus maiores alias consequatur aut perferendis doloribus asperiores repellat + +{% include 'chart.html' %} + +{% include 'piechart.html' %} + +{% include 'map.html' %} + +Add more analysis here + + +{% include 'supplementary_header.html' %} + +{% include 'citations.html' %} + + +### Additional Information + +- The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> + + +### Footnotes + +[^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. +[^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. +[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
\ No newline at end of file diff --git a/site/content/pages/datasets/uccs/assets/uccs_bboxes_clr_fill.jpg b/site/content/pages/datasets/uccs/assets/uccs_bboxes_clr_fill.jpg Binary files differdeleted file mode 100644 index c8002bb9..00000000 --- a/site/content/pages/datasets/uccs/assets/uccs_bboxes_clr_fill.jpg +++ /dev/null diff --git a/site/content/pages/datasets/uccs/assets/uccs_bboxes_grayscale.jpg b/site/content/pages/datasets/uccs/assets/uccs_bboxes_grayscale.jpg Binary files differdeleted file mode 100644 index 6e2833dd..00000000 --- a/site/content/pages/datasets/uccs/assets/uccs_bboxes_grayscale.jpg +++ /dev/null diff --git a/site/content/pages/datasets/uccs/assets/uccs_mean_bboxes_comp.jpg b/site/content/pages/datasets/uccs/assets/uccs_mean_bboxes_comp.jpg Binary files differnew file mode 100644 index 00000000..18f4c5ec --- /dev/null +++ b/site/content/pages/datasets/uccs/assets/uccs_mean_bboxes_comp.jpg diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index 092638c0..1e3ec097 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -2,11 +2,11 @@ status: published title: Unconstrained College Students -desc: <span class="dataset-name">Unconstrained College Students (UCCS)</span> is a dataset of images ... -subdesc: The UCCS dataset includes ... -slug: uccs +desc: <span class="dataset-name">Unconstrained College Students (UCCS)</span> is a dataset of long-range surveillance photos of students taken without their knowledge +subdesc: 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 cssclass: dataset image: assets/background.jpg +slug: uccs published: 2019-2-23 updated: 2019-2-23 authors: Adam Harvey @@ -15,16 +15,22 @@ authors: Adam Harvey ### sidebar -+ Collected: TBD -+ Published: TBD -+ Images: TBD -+ Faces: TBD ++ Published: 2018 ++ Images: 16,149 ++ Identities: 1,732 ++ Used for: Face recognition, face detection ++ Created by: Unviversity of Colorado Colorado Springs (US) ++ Funded by: ODNI, IARPA, ONR MURI, Amry SBIR, SOCOM SBIR ++ Website: <a href="https://vast.uccs.edu/Opensetface/">vast.uccs.edu</a> ## Unconstrained College Students ... (PAGE UNDER DEVELOPMENT) + + + {% include 'map.html' %} {% include 'chart.html' %} @@ -36,7 +42,6 @@ authors: Adam Harvey {% include 'citations.html' %} - ### Research Notes @@ -55,4 +60,15 @@ The more recent UCCS version of the dataset received funding from [^funding_uccs [^funding_sb]: 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.
\ No newline at end of file +[^funding_uccs]: Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3. + + +" 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. + +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. + +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. + +UnConstrained College Students (UCCS) Dataset + +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. "
\ No newline at end of file diff --git a/site/content/pages/research/01_from_1_to_100_pixels/index.md b/site/content/pages/research/01_from_1_to_100_pixels/index.md index a7b863a9..b219dffb 100644 --- a/site/content/pages/research/01_from_1_to_100_pixels/index.md +++ b/site/content/pages/research/01_from_1_to_100_pixels/index.md @@ -56,3 +56,55 @@ Ideas: - "Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf - IBM DiF: "Faces with region size less than 50x50 or inter-ocular distance of less than 30 pixels were discarded. Faces with non-frontal pose, or anything beyond being slightly tilted to the left or the right, were also discarded." + + + + +As the resolution +formatted as rectangular databases of 16 bit RGB-tuples or 8 bit grayscale values + + +To consider how visual privacy applies to real world surveillance situations, the first + +A single 8-bit grayscale pixel with 256 values is enough to represent the entire alphabet `a-Z0-9` with room to spare. + +A 2x2 pixels contains + +Using no more than a 42 pixel (6x7 image) face image researchers [cite] were able to correctly distinguish between a group of 50 people. Yet + +The likely outcome of face recognition research is that more data is needed to improve. Indeed, resolution is the determining factor for all biometric systems, both as training data to increase + +Pixels, typically considered the buiding blocks of images and vidoes, can also be plotted as a graph of sensor values corresponding to the intensity of RGB-calibrated sensors. + + +Wi-Fi and cameras presents elevated risks for transmitting videos and image documentation from conflict zones, high-risk situations, or even sharing on social media. How can new developments in computer vision also be used in reverse, as a counter-forensic tool, to minimize an individual's privacy risk? + +As the global Internet becomes increasingly effecient at turning the Internet into a giant dataset for machine learning, forensics, and data analysing, it would be prudent to also consider tools for decreasing the resolution. The Visual Defense module is just that. What are new ways to minimize the adverse effects of surveillance by dulling the blade. For example, a researcher paper showed that by decreasing a face size to 12x16 it was possible to do 98% accuracy with 50 people. This is clearly an example of + +This research module, tentatively called Visual Defense Tools, aims to explore the + + +### Prior Research + +- MPI visual privacy advisor +- NIST: super resolution +- YouTube blur tool +- WITNESS: blur tool +- Pixellated text +- CV Dazzle +- Bellingcat guide to geolocation +- Peng! magic passport + +### Notes + +- In China, out of the approximately 200 million surveillance cameras only about 15% have enough resolution for face recognition. +- In Apple's FaceID security guide, the probability of someone else's face unlocking your phone is 1 out of 1,000,000. +- In England, the Metropolitan Police reported a false-positive match rate of 98% when attempting to use face recognition to locate wanted criminals. +- In a face recognition trial at Berlin's Sudkreuz station, the false-match rate was 20%. + + +What all 3 examples illustrate is that face recognition is anything but absolute. In a 2017 talk, Jason Matheny the former directory of IARPA, admitted the face recognition is so brittle it can be subverted by using a magic marker and drawing "a few dots on your forehead". In fact face recognition is a misleading term. Face recognition is search engine for faces that can only ever show you the mos likely match. This presents real a real threat to privacy and lends + + +Globally, iPhone users unwittingly agree to 1/1,000,000 probably +relying on FaceID and TouchID to protect their information agree to a
\ No newline at end of file diff --git a/site/content/pages/research/02_what_computers_can_see/index.md b/site/content/pages/research/02_what_computers_can_see/index.md index ab4c7884..51621f46 100644 --- a/site/content/pages/research/02_what_computers_can_see/index.md +++ b/site/content/pages/research/02_what_computers_can_see/index.md @@ -100,6 +100,7 @@ A list of 100 things computer vision can see, eg: - Wearing Necktie - Wearing Necklace +for i in {1..9};do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_0$i.MP4;done;for i in {10..20}; do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_$i.MP4;done ## From Market 1501 @@ -149,4 +150,26 @@ Visibility boolean for each keypoint Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder) Body type (male, female or child) -source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/
\ No newline at end of file +source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/ + +## From Leeds Sports Pose + +=INDEX(A2:A9,MATCH(datasets!D1,B2:B9,0)) +=VLOOKUP(A2, datasets!A:J, 7, FALSE) + +Right ankle +Right knee +Right hip +Left hip +Left knee +Left ankle +Right wrist +Right elbow +Right shoulder +Left shoulder +Left elbow +Left wrist +Neck +Head top + +source: http://web.archive.org/web/20170915023005/sam.johnson.io/research/lsp.html
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