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diff --git a/site/content/pages/about/assets/adam-harvey-3d.jpg b/site/content/pages/about/assets/adam-harvey-3d.jpg Binary files differnew file mode 100644 index 00000000..2d7cbd80 --- /dev/null +++ b/site/content/pages/about/assets/adam-harvey-3d.jpg diff --git a/site/content/pages/about/assets/adam-harvey-3d.png b/site/content/pages/about/assets/adam-harvey-3d.png Binary files differnew file mode 100644 index 00000000..3616e851 --- /dev/null +++ b/site/content/pages/about/assets/adam-harvey-3d.png diff --git a/site/content/pages/about/assets/adam-harvey.jpg b/site/content/pages/about/assets/adam-harvey.jpg Binary files differindex e0ab893a..38a484d1 100644 --- a/site/content/pages/about/assets/adam-harvey.jpg +++ b/site/content/pages/about/assets/adam-harvey.jpg diff --git a/site/content/pages/about/assets/jules-laplace-3d.jpg b/site/content/pages/about/assets/jules-laplace-3d.jpg Binary files differnew file mode 100644 index 00000000..d51e0933 --- /dev/null +++ b/site/content/pages/about/assets/jules-laplace-3d.jpg diff --git a/site/content/pages/about/assets/jules-laplace.jpg b/site/content/pages/about/assets/jules-laplace.jpg Binary files differindex 310b2783..18fc1170 100644 --- a/site/content/pages/about/assets/jules-laplace.jpg +++ b/site/content/pages/about/assets/jules-laplace.jpg diff --git a/site/content/pages/about/credits.md b/site/content/pages/about/credits.md new file mode 100644 index 00000000..bc2283fd --- /dev/null +++ b/site/content/pages/about/credits.md @@ -0,0 +1,48 @@ +------------ + +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 index c0baad71..f82a09a0 100644 --- a/site/content/pages/about/disclaimer.md +++ b/site/content/pages/about/disclaimer.md @@ -13,15 +13,18 @@ authors: Adam Harvey # Disclaimer -- [About](/about/) -- [Press](/about/press/) -- [Disclaimer](/about/disclaimer/) -- [Terms and Conditions](/about/terms/) -- [Privacy Policy](/about/privacy/) +<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> -### Sidebar - -## End Sidebar +(TEMPORARY PAGE) Last updated: December 04, 2018 diff --git a/site/content/pages/about/index.md b/site/content/pages/about/index.md index ccb6ed0d..4fec0777 100644 --- a/site/content/pages/about/index.md +++ b/site/content/pages/about/index.md @@ -13,28 +13,44 @@ authors: Adam Harvey # About MegaPixels -{% include 'about_navigation.html' %} +<section class="about-menu"> +<ul> +<li><a class="current" href="/about/">About</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> +</ul> +</section> -MegaPixels is an art and research project by Adam Harvey about the origins and ethics of facial analysis datasets. Where do they come from? Who's included? Who created it and for what reason? +(PAGE UNDER DEVELOPMENT) -MegaPixels sets out to answer to these questions and reveal the stories behind the millions of images used to train, evaluate, and power the facial recognition surveillance algorithms used today. MegaPixels is authored by Adam Harvey, developed in collaboration with Jules LaPlace, and produced in partnership with Mozilla. +<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> -MegaPixels sets out to answer to these questions and reveal the stories behind the millions of images used to train, evaluate, and power the facial recognition surveillance algorithms used today. MegaPixels is authored by Adam Harvey, developed in collaboration with Jules LaPlace, and produced in partnership with Mozilla. +Collectively, facial recognition datasets are now gathered "in the wild". -Notes +<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> -- critical but informative -- not anti-dataset -- pro-sharing, pro-public dataset -- w/o data -- not generally anti-researcher, their parent organization should have checks in place to prevent dubious dataset collection methods -- +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). - **Adam Harvey** is an American artist and researcher based in Berlin. 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 awards, and is currently a researcher in residence at Karlsruhe HfG studying artifical intelligence and datasets. - -**Jules LaPlace** is an American artist and technologist also based in Berlin. He was previously the CTO of a NYC digital agency and currently works at VFRAME, developing computer vision for human rights groups, and building creative software for artists. - -## Partnership - -MegaPixels is produced in partnership with **Mozilla**, a free software community founded in 1998 by members of Netscape. The Mozilla community uses, develops, spreads and supports Mozilla products, thereby promoting exclusively free software and open standards, with only minor exceptions. The community is supported institutionally by the not-for-profit Mozilla Foundation and its tax-paying subsidiary, the Mozilla Corporation. +<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> + </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> + </div> +</div> diff --git a/site/content/pages/about/press.md b/site/content/pages/about/press.md index abd4d823..47e1af52 100644 --- a/site/content/pages/about/press.md +++ b/site/content/pages/about/press.md @@ -2,7 +2,7 @@ status: published title: MegaPixels Press and News -desc: MegaPixels in The News +desc: MegaPixels Press and News slug: press cssclass: about published: 2018-12-04 @@ -13,8 +13,17 @@ authors: Adam Harvey # Press -{% include 'about_navigation.html' %} +<section class="about-menu"> +<ul> +<li><a href="/about/">About</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> +</ul> +</section> -(list of press articles and images will go here) +(TEMPORARY PAGE) - Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent <https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset>
\ No newline at end of file diff --git a/site/content/pages/about/privacy.md b/site/content/pages/about/privacy.md index a47b74cc..e36daf2a 100644 --- a/site/content/pages/about/privacy.md +++ b/site/content/pages/about/privacy.md @@ -13,7 +13,18 @@ authors: Adam Harvey # Privacy Policy -{% include 'about_navigation.html' %} +<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 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> +</ul> +</section> + +(TEMPORARY PAGE) A summary of our privacy policy is as follows: diff --git a/site/content/pages/about/research.md b/site/content/pages/about/research.md deleted file mode 100644 index 8f001cc9..00000000 --- a/site/content/pages/about/research.md +++ /dev/null @@ -1,16 +0,0 @@ ------------- - -status: published -title: About MegaPixels Research Methodologies -desc: About MegaPixels Research Methodologies -slug: resewarch -cssclass: about -published: 2018-12-04 -updated: 2018-12-04 -authors: Adam Harvey - ------------- - -# Research Methodologies - -{% include 'about_navigation.html' %}
\ No newline at end of file diff --git a/site/content/pages/about/terms.md b/site/content/pages/about/terms.md index 38d43735..7ae6dac7 100644 --- a/site/content/pages/about/terms.md +++ b/site/content/pages/about/terms.md @@ -14,7 +14,18 @@ authors: Adam Harvey # Terms and Conditions ("Terms") -{% include 'about_navigation.html' %} +<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 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> +</ul> +</section> + +(TEMPORARY PAGE) (FPO: this is only example text) diff --git a/site/content/pages/datasets/50_people_one_question/index.md b/site/content/pages/datasets/50_people_one_question/index.md index e7dec0aa..2276e386 100644 --- a/site/content/pages/datasets/50_people_one_question/index.md +++ b/site/content/pages/datasets/50_people_one_question/index.md @@ -1,6 +1,6 @@ ------------ -status: published +status: draft title: 50 People One Question desc: <span style="color:#ffaa00">People One Question</span> is a dataset of people from an online video series on YouTube and Vimeo used for building facial recogntion algorithms subdesc: People One Question dataset includes ... @@ -23,9 +23,11 @@ authors: Adam Harvey ## 50 People 1 Question - 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. +(PAGE UNDER DEVELOPMENT) - 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 +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 'map.html' %} diff --git a/site/content/pages/datasets/brainwash/assets/background.jpg b/site/content/pages/datasets/brainwash/assets/background.jpg Binary files differindex 8f2de697..e6393ab5 100755..100644 --- a/site/content/pages/datasets/brainwash/assets/background.jpg +++ b/site/content/pages/datasets/brainwash/assets/background.jpg diff --git a/site/content/pages/datasets/brainwash/assets/background_540.jpg b/site/content/pages/datasets/brainwash/assets/background_540.jpg Binary files differnew file mode 100644 index 00000000..5c8c0ad4 --- /dev/null +++ b/site/content/pages/datasets/brainwash/assets/background_540.jpg diff --git a/site/content/pages/datasets/brainwash/assets/background_600.jpg b/site/content/pages/datasets/brainwash/assets/background_600.jpg Binary files differnew file mode 100755 index 00000000..8f2de697 --- /dev/null +++ b/site/content/pages/datasets/brainwash/assets/background_600.jpg diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index 5fe0da4c..816485d7 100644 --- 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 people from webcams the Brainwash Cafe in San Francisco being used to train face detection algorithms -subdesc: Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" +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 slug: brainwash cssclass: dataset color: #ffaa00 @@ -17,22 +17,22 @@ authors: Adam Harvey ### sidebar -+ Collected: 2014 + Published: 2015 + Images: 11,918 + Faces: 91,146 + Created by: Stanford Department of Computer Science + Funded by: Max Planck Center for Visual Computing and Communication -+ Resolution: 640x480px -+ Size: 4.1GB -+ Origin: Brainwash Cafe, San Franscisco ++ Location: Brainwash Cafe, San Franscisco + Purpose: Training face detection + 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 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. 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. @@ -43,13 +43,17 @@ Since it's publication by Stanford in 2015, the Brainwash dataset has appeared i  +{% include 'chart.html' %} + {% include 'map.html' %} +Add more analysis here + + {% include 'supplementary_header.html' %} {% include 'citations.html' %} -{% include 'chart.html' %} ### Additional Information diff --git a/site/content/pages/datasets/celeba/index.md b/site/content/pages/datasets/celeba/index.md index 19b0291d..a2669cf6 100644 --- a/site/content/pages/datasets/celeba/index.md +++ b/site/content/pages/datasets/celeba/index.md @@ -1,6 +1,6 @@ ------------ -status: published +status: draft title: CelebA desc: <span style="color:#ffaa00">CelebA</span> is a dataset of people... subdesc: CelebA includes... @@ -23,6 +23,8 @@ authors: Adam Harvey ## CelebA +(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 diff --git a/site/content/pages/datasets/cofw/index.md b/site/content/pages/datasets/cofw/index.md index 3b1cdb2b..d017f405 100644 --- a/site/content/pages/datasets/cofw/index.md +++ b/site/content/pages/datasets/cofw/index.md @@ -1,6 +1,6 @@ ------------ -status: published +status: draft title: Caltech Occluded Faces in The Wild desc: COFW: Caltech Occluded Faces in The Wild slug: cofw @@ -10,7 +10,8 @@ authors: Adam Harvey ------------ -# Caltech Occluded Faces in The Wild + +### sidebar + Years: 1993-1996 + Images: 14,126 @@ -19,9 +20,9 @@ authors: Adam Harvey + Funded by: ODNI, IARPA, Microsoft -<!--header--> +## Caltech Occluded Faces in the Wild - +(PAGE UNDER DEVELOPMENT) COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion]. diff --git a/site/content/pages/datasets/kitti/index.md b/site/content/pages/datasets/kitti/index.md deleted file mode 100644 index 25d0da69..00000000 --- a/site/content/pages/datasets/kitti/index.md +++ /dev/null @@ -1,42 +0,0 @@ ------------- - -status: published -title: KITTI -desc: <span style="color:#ffaa00">Kitti</span> TBD -subdesc: TBD -slug: kitti -cssclass: dataset -image: assets/background.jpg -year: 2015 -published: 2019-2-23 -updated: 2019-2-23 -authors: Adam Harvey - ------------- - -### sidebar - -+ Collected: TBD - -## Kitti - -add text - -{% include 'map.html' %} - - -{% 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/lfw/index.md b/site/content/pages/datasets/lfw/index.md index 833c6963..b803efc5 100644 --- a/site/content/pages/datasets/lfw/index.md +++ b/site/content/pages/datasets/lfw/index.md @@ -2,15 +2,12 @@ status: published title: Labeled Faces in The Wild -desc: Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition. -subdesc: It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004. +desc: Labeled Faces in The Wild (LFW) is the first facial recognition dataset created entirely from online photos +subdesc: It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004 and is the most frequently used dataset in the world for benchmarking face recognition algorithms. image: assets/background.jpg slug: lfw -<<<<<<< HEAD year: 2007 -======= color: #ff0000 ->>>>>>> e6c50e5550275b8e9d2245201c77c6f9fef7a11a published: 2019-2-23 updated: 2019-2-23 authors: Adam Harvey @@ -36,6 +33,8 @@ authors: Adam Harvey ## Labeled Faces in the Wild +(PAGE UNDER DEVELOPMENT) + *Labeled Faces in The Wild* (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of *Names of Faces* and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are... @@ -52,11 +51,12 @@ The *Names and Faces* dataset was the first face recognition dataset created ent {% include 'map.html' %} +{% include 'chart.html' %} + {% include 'supplementary_header.html' %} {% include 'citations.html' %} -{% include 'chart.html' %} ### Commercial Use diff --git a/site/content/pages/datasets/mars/index.md b/site/content/pages/datasets/mars/index.md index 19f9ced4..2b3192f3 100644 --- a/site/content/pages/datasets/mars/index.md +++ b/site/content/pages/datasets/mars/index.md @@ -2,8 +2,8 @@ status: published title: MARS -desc: <span style="color:#ffaa00">MARS</span> is a dataset of people... -subdesc: MARS includes... +desc: <span style="color:#99ccee">Motion Analysis and Re-identification Set (MARS)</span> is a dataset is collection of CCTV footage +subdesc: The MARS dataset includes 1,191,003 of people used for training person re-identification algorithms slug: mars cssclass: dataset image: assets/background.jpg @@ -21,7 +21,9 @@ authors: Adam Harvey + Faces: TBD -## 50 MARS +## Motion Analysis and Re-identification Set (MARS) + +(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. diff --git a/site/content/pages/datasets/viper/assets/index.jpg b/site/content/pages/datasets/viper/assets/index.jpg Binary files differnew file mode 100644 index 00000000..6eaa365c --- /dev/null +++ b/site/content/pages/datasets/viper/assets/index.jpg diff --git a/site/content/pages/datasets/viper/index.md b/site/content/pages/datasets/viper/index.md new file mode 100644 index 00000000..574fe65c --- /dev/null +++ b/site/content/pages/datasets/viper/index.md @@ -0,0 +1,40 @@ +------------ + +status: published +title: VIPeR +desc: VIPeR is a person re-identification dataset of images captured at UC Santa Cruz in 2007 +subdesc: VIPeR contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance +slug: viper +cssclass: dataset +color: #ffaa00 +image: assets/background.jpg +year: 2007 +published: 2019-2-23 +updated: 2019-2-23 +authors: Adam Harvey + +------------ + +### sidebar + ++ Published: 2007 ++ Images: 1,264 ++ Persons: 632 ++ Created by: UC Santa Cruz + +## VIPeR Dataset + +(PAGE UNDER DEVELOPMENT) + +*VIPeR (Viewpoint Invariant Pedestrian Recognition)* is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way." + +VIPeR is amongst the most widely used publicly available person re-identification datasets. In 2017 the VIPeR dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute). + + +{% include 'chart.html' %} + +{% include 'map.html' %} + +{% include 'supplementary_header.html' %} + +{% include 'citations.html' %}
\ No newline at end of file diff --git a/site/content/pages/research/00_introduction/index.md b/site/content/pages/research/00_introduction/index.md index 6fec7ab5..bcb3d57c 100644 --- a/site/content/pages/research/00_introduction/index.md +++ b/site/content/pages/research/00_introduction/index.md @@ -1,6 +1,6 @@ ------------ -status: published +status: draft title: 00: Introduction desc: Introduction to Megapixels slug: 00_introduction @@ -16,6 +16,22 @@ authors: Megapixels + Author: Adam Harvey + +### Motivation + +Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to developing and validating face recognition technologies. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">videos on YouTube</a>. + +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. + +While many of these datasets include public figures such as politicians, athletes, and actors; they also include many non-public figures: digital activists, students, pedestrians, and semi-private shared photo albums are all considered "in the wild" and fair game for research projects. Some images are used with creative commons licenses, yet others were taken in unconstrained scenarios without awareness or consent. At first glance it appears many of the datasets were created for seemingly harmless academic research, but when examined further it becomes clear that they're also used by foreign defense agencies. + +The MegaPixels site is based on an earlier [installation](https://ahprojects.com/megapixels-glassroom) (also supported by Mozilla) at the [Tactical Tech Glassroom](https://theglassroom.org/) in London in 2017; and a commission from the Elevate arts festival curated by Berit Gilma about pedestrian recognition datasets in 2018, and research during [CV Dazzle](https://cvdazzle.com) from 2010-2015. Through the many prototypes, conversations, pitches, PDFs, and false starts this project has endured during the last 5 years, it eventually evolved into something much different than originally imagined. Now, as datasets become increasingly influential in shaping the computational future, it's clear that they must be critically analyzed to understand the biases, shortcomings, funding sources, and contributions to the surveillance industry. However, it's misguided to only criticize these datasets for their flaws without also praising their contribution to society. Without publicly available facial analysis datasets there would be less public discourse, less open-source software, and less peer-reviewed research. Public datasets can indeed become a vital public good for the information economy but as this projects aims to illustrate, many ethical questions arise about consent, intellectual property, surveillance, and privacy. + +<!-- who provided funding to research, development this project understand the role these datasets have played in creating biometric surveillance technologies. --> + + + + Ever since the first computational facial recognition research project by the CIA in the early 1960s, data has always played a vital role in the development of our biometric future. Without facial recognition datasets there would be no facial recognition. Datasets are an indispensable part of any artificial intelligence system because, as Geoffrey Hinton points out: > Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - [Geoffrey Hinton](https://www.youtube.com/watch?v=-eyhCTvrEtE) @@ -26,7 +42,7 @@ Algorithms learn from datasets. And we program algorithms by building datasets. Ignore content below these lines ----- -
+ It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating. 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 409dcf02..a7b863a9 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 @@ -1,6 +1,6 @@ ------------ -status: published +status: draft title: From 1 to 100 Pixels desc: High resolution insights from low resolution imagery tagline: A breif description of this post, appears in the index page overview @@ -54,4 +54,5 @@ Ideas: [^nist_sres]: NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips -- "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
\ No newline at end of file +- "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." 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 c289e16b..ab4c7884 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 @@ -99,3 +99,54 @@ A list of 100 things computer vision can see, eg: - Wearing Earrings - Wearing Necktie - Wearing Necklace + + +## From Market 1501 + +The 27 attributes are: + +| attribute | representation in file | label | +| :----: | :----: | :----: | +| gender | gender | male(1), female(2) | +| hair length | hair| short hair(1), long hair(2) | +| sleeve length | up | long sleeve(1), short sleeve(2) | +| length of lower-body clothing | down | long lower body clothing(1), short(2) | +| type of lower-body clothing| clothes| dress(1), pants(2) | +| wearing hat| hat | no(1), yes(2) | +| carrying backpack| backpack | no(1), yes(2) | +| carrying bag| bag | no(1), yes(2) | +| carrying handbag| handbag | no(1), yes(2) | +| age| age | young(1), teenager(2), adult(3), old(4) | +| 8 color of upper-body clothing| upblack, upwhite, upred, uppurple, upyellow, upgray, upblue, upgreen | no(1), yes(2) | +| 9 color of lower-body clothing| downblack, downwhite, downpink, downpurple, downyellow, downgray, downblue, downgreen,downbrown | no(1), yes(2) | + +source: https://github.com/vana77/Market-1501_Attribute/blob/master/README.md + +## From DukeMTMC + +The 23 attributes are: + +| attribute | representation in file | label | +| :----: | :----: | :----: | +| gender | gender | male(1), female(2) | +| length of upper-body clothing | top | short upper body clothing(1), long(2) | +| wearing boots| boots| no(1), yes(2) | +| wearing hat| hat | no(1), yes(2) | +| carrying backpack| backpack | no(1), yes(2) | +| carrying bag| bag | no(1), yes(2) | +| carrying handbag| handbag | no(1), yes(2) | +| color of shoes| shoes | dark(1), light(2) | +| 8 color of upper-body clothing| upblack, upwhite, upred, uppurple, upgray, upblue, upgreen, upbrown | no(1), yes(2) | +| 7 color of lower-body clothing| downblack, downwhite, downred, downgray, downblue, downgreen, downbrown | no(1), yes(2) | + +source: https://github.com/vana77/DukeMTMC-attribute/blob/master/README.md + +## From H3D Dataset + +The joints and other keypoints (eyes, ears, nose, shoulders, elbows, wrists, hips, knees and ankles) +The 3D pose inferred from the keypoints. +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/
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