diff options
34 files changed, 741 insertions, 158 deletions
diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 276280b1..12b059ef 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -118,20 +118,20 @@ header .links a { margin-right: 32px; transition: color 0.1s cubic-bezier(0,0,1,1), border-color 0.05s cubic-bezier(0,0,1,1); border-bottom: 1px solid rgba(255,255,255,0); - padding: 3px; + padding-bottom: 3px; font-weight: 400; } header .links a.active { color: #fff; - border-bottom: 1px solid rgba(255,255,255,255); + border-bottom: 2px solid rgba(255,255,255,255); } .desktop header .links a:hover { color: #fff; - border-bottom: 1px solid rgba(255,255,255,255); + border-bottom: 2px solid rgba(255,255,255,255); } .desktop header .links a.active:hover { color: #fff; - border-bottom: 1px solid rgba(255,255,255,255); + border-bottom: 2px solid rgba(255,255,255,255); } header .links.splash{ font-size:22px; @@ -146,10 +146,10 @@ footer { display: flex; flex-direction: row; justify-content: space-between; - color: #888; - font-size: 9pt; + color: #666; + font-size: 11px; line-height: 17px; - padding: 20px 20px; + padding: 15px; font-family: "Roboto", sans-serif; } footer > div { @@ -164,14 +164,40 @@ footer > div:nth-child(2) { } footer a { display: inline-block; - color: #888; + color: #ccc; transition: color 0.1s cubic-bezier(0,0,1,1); - margin-right: 5px; + border-bottom:1px solid #555; + padding-bottom: 1px; + text-decoration: none; +} + +footer a:hover{ + color: #ccc; + border-bottom:1px solid #999; +} +footer ul{ + margin:0; +} +footer ul li{ + color: #bbb; + margin: 0 5px 0 0; + font-size: 12px; + display: inline-block; +} +footer ul li:last-child{ + margin-right:0px; +} +footer ul.footer-left{ + float:left; + margin-left:40px; +} +footer ul.footer-right{ + float:right; + margin-right:40px; } .desktop footer a:hover { color: #ddd; } - /* headings */ h1 { @@ -293,7 +319,7 @@ p.subp{ font-size: 14px; } .content a { - color: #fff; + color: #dedede; text-decoration: none; border-bottom: 2px solid #666; padding-bottom: 1px; @@ -746,6 +772,7 @@ section.fullwidth .image { display: flex; flex-direction: row; flex-wrap: wrap; + margin:0; } .dataset-list a { text-decoration: none; @@ -1080,18 +1107,19 @@ ul.map-legend li.source:before { /* footnotes */ a.footnote { - font-size: 10px; + font-size: 9px; + line-height: 0px; position: relative; - display: inline-block; - bottom: 10px; + /*display: inline-block;*/ + bottom: 7px; text-decoration: none; color: #ff8; border: 0; - left: 2px; + left: -1px; transition-duration: 0s; } a.footnote_shim { - display: inline-block; + /*display: inline-block;*/ width: 1px; height: 1px; overflow: hidden; position: relative; diff --git a/site/content/pages/about/attribution.md b/site/content/pages/about/attribution.md index cf537ad4..5060b2d9 100644 --- a/site/content/pages/about/attribution.md +++ b/site/content/pages/about/attribution.md @@ -32,7 +32,7 @@ If you use the MegaPixels data or any data derived from it, please cite the orig title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} } </pre> diff --git a/site/content/pages/about/index.md b/site/content/pages/about/index.md index f68008cc..a6ce3d3d 100644 --- a/site/content/pages/about/index.md +++ b/site/content/pages/about/index.md @@ -24,8 +24,9 @@ authors: Adam Harvey MegaPixels is an independent art and research project by Adam Harvey and Jules LaPlace that investigates the ethics, origins, and individual privacy implications of face recognition image datasets and their role in the expansion of biometric surveillance technologies. +MegaPixels is made possible with support from <a href="http://mozilla.org">Mozilla</a>, our primary funding partner. -The MegaPixels site is made possible with support from <a href="http://mozilla.org">Mozilla</a> +Additional support for MegaPixels is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center, 1-year research-in-residence grant from Karlsruhe HfG, and sales from the Privacy Gift Shop. <div class="flex-container team-photos-container"> @@ -85,6 +86,16 @@ You are free: Please direct questions, comments, or feedback to [mastodon.social/@adamhrv](https://mastodon.social/@adamhrv) +#### Funding Partners + +The MegaPixels website, research, and development is made possible with support form Mozilla, our primary funding partner. + +[ add logos ] + +Additional support is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center and a 1-year research-in-residence grant from Karlsruhe HfG. + +[ add logos ] + ##### Attribution If you use MegaPixels or any data derived from it for your work, please cite our original work as follows: diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index b57bcdf4..75b0c006 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -8,8 +8,8 @@ slug: brainwash cssclass: dataset image: assets/background.jpg year: 2015 -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ @@ -25,24 +25,20 @@ The Brainwash dataset is unique because it uses images from a publicly available Although Brainwash appears to be a less popular dataset, it was used in 2016 and 2017 by researchers from the National University of Defense Technology in China took note of the dataset and used it for two [research](https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c) [projects](https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b) on advancing the capabilities of object detection to more accurately isolate the target region in an image ([PDF](https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_05006.pdf)). [^localized_region_context] [^replacement_algorithm]. The dataset also appears in a 2017 [research paper](https://ieeexplore.ieee.org/document/7877809) from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes". - - + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} + - - - - + {% include 'cite_our_work.html' %} ### Footnotes - [^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. [^end_to_end]: Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. [^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. diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 0f4986de..2420d042 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -7,8 +7,8 @@ subdesc: Duke MTMC contains over 2 million video frames and 2,700 unique identit slug: duke_mtmc cssclass: dataset image: assets/background.jpg -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index d5e52952..0c78e094 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -2,14 +2,14 @@ status: published title: Microsoft 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 +desc: Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition technologies +subdesc: The MS Celeb dataset includes over 10 million images of about 100K people and a target list of 1 million individuals slug: msceleb cssclass: dataset image: assets/background.jpg year: 2015 -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ @@ -19,22 +19,81 @@ authors: Adam Harvey ### sidebar ### end sidebar +Microsoft Celeb (MS Celeb) is a dataset of 10 million face images scraped from the Internet and used for research and development of large-scale biometric recognition systems. According to Microsoft Research who created and published the [dataset](http://msceleb.org) in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of nearly 100,000 individuals. Microsoft's goal in building this dataset was to distribute the initial training dataset of 100,000 individuals images and use this to accelerate reserch into recognizing a target list of one million individuals from their face images "using all the possibly collected face images of this individual on the web as training data".[^msceleb_orig] -https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology +These one million people, defined as Micrsoft Research as "celebrities", are often merely people who must maintain an online presence for their professional lives. Microsoft's list of 1 million people is an expansive exploitation of the current reality that for many people including academics, policy makers, writers, artists, and especially journalists maintaining an online presence is mandatory and should not allow Microsoft (or anyone else) to use their biometrics for reserach and development of surveillance technology. Many of names in target list even include people critical of the very technology Microsoft is using their name and biometric information to build. The list includes digital rights activists like Jillian York and [add more]; artists critical of surveillance including Trevor Paglen, Hito Steryl, Kyle McDonald, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glen Greenwald; Data and Society founder danah boyd; and even Julie Brill the former FTC commissioner responsible for protecting consumer’s privacy to name a few. -https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm +### Microsoft's 1 Million Target List -{% include 'dashboard.html' %} +Below is a list of names that were included in list of 1 million individuals curated to illustrate Microsoft's expansive and exploitative practice of scraping the Internet for biometric training data. The entire name file can be downloaded from [msceleb.org](https://msceleb.org). Names appearing with * indicate that Microsoft also distributed imaged. -{% include 'supplementary_header.html' %} +[ cleaning this up ] + +=== columns 2 + +| Name | ID | Profession | Images | +| --- | --- | --- | --- | +| Jeremy Scahill | /m/02p_8_n | Journalist | x | +| Jillian York | /m/0g9_3c3 | Digital rights activist | x | +| Astra Taylor | /m/05f6_39 | Author, activist | x | +| Jonathan Zittrain | /m/01f75c | EFF board member | no | +| Julie Brill | x | x | x | +| Jonathan Zittrain | x | x | x | +| Bruce Schneier | m.095js | Cryptologist and author | yes | +| Julie Brill | m.0bs3s9g | x | x | +| Kim Zetter | /m/09r4j3 | x | x | +| Ethan Zuckerman | x | x | x | +| Jill Magid | x | x | x | +| Kyle McDonald | x | x | x | +| Trevor Paglen | x | x | x | +| R. Luke DuBois | x | x | x | + +==== + +| Name | ID | Profession | Images | +| --- | --- | --- | -- | +| Trevor Paglen | x | x | x | +| Ai Weiwei | /m/0278dyq | x | x | +| Jer Thorp | /m/01h8lg | x | x | +| Edward Felten | /m/028_7k | x | x | +| Evgeny Morozov | /m/05sxhgd | Scholar and technology critic | yes | +| danah boyd | /m/06zmx5 | Data and Society founder | x | +| Bruce Schneier | x | x | x | +| Laura Poitras | x | x | x | +| Trevor Paglen | x | x | x | +| Astra Taylor | x | x | x | +| Shoshanaa Zuboff | x | x | x | +| Eyal Weizman | m.0g54526 | x | x | +| Aram Bartholl | m.06_wjyc | x | x | +| James Risen | m.09pk6b | x | x | + +=== end columns + +After publishing this list, researchers from Microsoft Asia then worked with researchers affilliated with China's National University of Defense Technology (controlled by China's Central Military Commission) and used the the MS Celeb dataset for their [research paper](https://www.semanticscholar.org/paper/Faces-as-Lighting-Probes-via-Unsupervised-Deep-Yi-Zhu/b301fd2fc33f24d6f75224e7c0991f4f04b64a65) on using "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction" with potential applications in 3D face recognition. + +In an article published by the Financial Times based on data discovered during this investigation, Samm Sacks (senior fellow at New American and China tech policy expert) commented that this research raised "red flags because of the nature of the technology, the authors affilliations, combined with the what we know about how this technology is being deployed in China right now".[^madhu_ft] -### Additional Information +Four more papers published by SenseTime which also use the MS Celeb dataset raise similar flags. SenseTime is Beijing based company providing surveillance to Chinese authorities including [ add context here ] has been [flagged](https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html) as complicity in potential human rights violations. -- The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> +One of the 4 SenseTime papers, "Exploring Disentangled Feature Representation Beyond Face Identification", shows how SenseTime is developing automated face analysis technology to infer race, narrow eyes, nose size, and chin size, all of which could be used to target vulnerable ethnic groups based on their facial appearances.[^disentangled] +Earlier in 2019, Microsoft CEO [Brad Smith](https://blogs.microsoft.com/on-the-issues/2018/12/06/facial-recognition-its-time-for-action/) called for the governmental regulation of face recognition, citing the potential for misuse, a rare admission that Microsoft's surveillance-driven business model had lost its bearing. More recently Smith also [announced](https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV) that Microsoft would seemingly take stand against potential misuse and decided to not sell face recognition to an unnamed United States law enforcement agency, citing that their technology was not accurate enough to be used on minorities because it was trained mostly on white male faces. + +What the decision to block the sale announces is not so much that Microsoft has upgraded their ethics, but that it publicly acknolwedged it can't sell a data-driven product without data. Microsoft can't sell face recognition for faces they can't train on. + +Until now, that data has been freely harvested from the Internet and packaged in training sets like MS Celeb, which are overwhelmingly [white](https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html) and [male](https://gendershades.org). Without balanced data, facial recognition contains blind spots. And without datasets like MS Celeb, the powerful yet innaccurate facial recognition services like Microsoft's Azure Cognitive Service also would not be able to see at all. + +Microsoft didn't only create MS Celeb for other researchers to use, they also used it internally. In a publicly available 2017 Microsoft Research project called "([One-shot Face Recognition by Promoting Underrepresented Classes](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/))", Microsoft leveraged the MS Celeb dataset to analyse their algorithms and advertise the results. Interestingly, the Microsoft's [corporate version](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/) does not mention they used the MS Celeb datset, but the [open-acess version](https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70) of the paper published on arxiv.org that same year explicity mentions that Microsoft Research tested their algorithms "on the MS-Celeb-1M low-shot learning benchmark task." + +We suggest that if Microsoft Research wants biometric data for surveillance research and development, they should start with own researcher's biometric data instead of scraping the Internet for journalists, artists, writers, and academics. + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} ### 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 +[^brad_smith]: Brad Smith cite +[^msceleb_orig]: MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition +[^madhu_ft]: Microsoft worked with Chinese military university on artificial intelligence +[^disentangled]: "Exploring Disentangled Feature Representation Beyond Face Identification"
\ No newline at end of file diff --git a/site/content/pages/datasets/oxford_town_centre/index.md b/site/content/pages/datasets/oxford_town_centre/index.md index c32cd022..21d3d949 100644 --- a/site/content/pages/datasets/oxford_town_centre/index.md +++ b/site/content/pages/datasets/oxford_town_centre/index.md @@ -19,7 +19,7 @@ authors: Adam Harvey ### sidebar ### end sidebar -The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.[^ben_benfold_orig] The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009[^guiding_surveillance] the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. +The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.[^ben_benfold_orig] The CCTV video was obtained from a surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009[^guiding_surveillance] the [Oxford Town Centre dataset](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project. @@ -29,9 +29,9 @@ The Oxford Town Centre dataset is unique in that it uses footage from a public s ### Location -The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs [source](https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656). At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their [research projects](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) it is likely that they would also be able to access to this camera. +The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs [source](https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656). At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their [research projects](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) it is increasingly likely that they would also be able to access to this camera. -To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera [pointing in the same direction](https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/) as the Oxford Town Centre dataset proving the camera can and has been rotated before. +Next, to discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera [pointing in the same direction](https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/) as the Oxford Town Centre dataset proving the camera can and has been rotated before. As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse ([photo](https://www.flickr.com/photos/katieportwin/364492063/in/photolist-4meWFE-yd7rw-yd7X6-5sDHuc-yd7DN-59CpEK-5GoHAc-yd7Zh-3G2uJP-yd7US-5GomQH-4peYpq-4bAEwm-PALEr-58RkAp-5pHEkf-5v7fGq-4q1J9W-4kypQ2-5KX2Eu-yd7MV-yd7p6-4McgWb-5pJ55w-24N9gj-37u9LK-4FVcKQ-a81Enz-5qNhTG-59CrMZ-2yuwYM-5oagH5-59CdsP-4FVcKN-4PdxhC-5Lhr2j-2PAd2d-5hAwvk-zsQSG-4Cdr4F-3dUPEi-9B1RZ6-2hv5NY-4G5qwP-HCHBW-4JiuC4-4Pdr9Y-584aEV-2GYBEc-HCPkp/), [history](http://www.oxfordhistory.org.uk/cornmarket/west/47_51.html)) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a [GAP website snapshot](web.archive.org/web/20081201002524/http://www.gap.com/) from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal. diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index de2cec4d..d37db132 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -9,8 +9,8 @@ image: assets/background.jpg cssclass: dataset image: assets/background.jpg slug: uccs -published: 2019-2-23 -updated: 2019-4-15 +published: 2019-4-18 +updated: 2019-4-19 authors: Adam Harvey ------------ 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 51621f46..faa4ab17 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 @@ -25,6 +25,13 @@ A list of 100 things computer vision can see, eg: - tired, drowsiness in car - affectiva: interest in product, intent to buy +## From SenseTime paper + +Exploring Disentangled Feature Representation Beyond Face Identification + +From https://arxiv.org/pdf/1804.03487.pdf +The attribute IDs from 1 to 40 corre-spond to: ‘5 o Clock Shadow’, ‘Arched Eyebrows’, ‘Attrac-tive’, ‘Bags Under Eyes’, ‘Bald’, ‘Bangs’, ‘Big Lips’, ‘BigNose’, ‘Black Hair’, ‘Blond Hair’, ‘Blurry’, ‘Brown Hair’,‘Bushy Eyebrows’, ‘Chubby’, ‘Double Chin’, ‘Eyeglasses’,‘Goatee’, ‘Gray Hair’, ‘Heavy Makeup’, ‘High Cheek-bones’, ‘Male’, ‘Mouth Slightly Open’, ‘Mustache’, ‘Nar-row Eyes’, ‘No Beard’, ‘Oval Face’, ‘Pale Skin’, ‘PointyNose’, ‘Receding Hairline’, ‘Rosy Cheeks’, ‘Sideburns’,‘Smiling’, ‘Straight Hair’, ‘Wavy Hair’, ‘Wearing Ear-rings’, ‘Wearing Hat’, ‘Wearing Lipstick’, ‘Wearing Neck-lace’, ‘Wearing Necktie’ and ‘Young’. It’ + ## From PubFig Dataset diff --git a/site/public/about/attribution/index.html b/site/public/about/attribution/index.html index 5fe92b8d..7b09e5b4 100644 --- a/site/public/about/attribution/index.html +++ b/site/public/about/attribution/index.html @@ -42,7 +42,7 @@ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} } </pre><p>and include this license and attribution protocol within any derivative work.</p> <p>If you publish data derived from MegaPixels, the original dataset creators should first be notified.</p> diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html index 79411122..76d5b92f 100644 --- a/site/public/datasets/50_people_one_question/index.html +++ b/site/public/datasets/50_people_one_question/index.html @@ -88,7 +88,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html index 7969c1d6..a3ff00cf 100644 --- a/site/public/datasets/afad/index.html +++ b/site/public/datasets/afad/index.html @@ -90,7 +90,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/brainwash/ijb_c/index.html b/site/public/datasets/brainwash/ijb_c/index.html new file mode 100644 index 00000000..f57d180b --- /dev/null +++ b/site/public/datasets/brainwash/ijb_c/index.html @@ -0,0 +1,152 @@ +<!doctype html> +<html> +<head> + <title>MegaPixels</title> + <meta charset="utf-8" /> + <meta name="author" content="Adam Harvey" /> + <meta name="description" content="IJB-C is a datset ..." /> + <meta name="referrer" content="no-referrer" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> + <link rel='stylesheet' href='/assets/css/fonts.css' /> + <link rel='stylesheet' href='/assets/css/css.css' /> + <link rel='stylesheet' href='/assets/css/leaflet.css' /> + <link rel='stylesheet' href='/assets/css/applets.css' /> +</head> +<body> + <header> + <a class='slogan' href="/"> + <div class='logo'></div> + <div class='site_name'>MegaPixels</div> + <div class='splash'>IJB-C</div> + </a> + <div class='links'> + <a href="/datasets/">Datasets</a> + <a href="/about/">About</a> + </div> + </header> + <div class="content content-dataset"> + + <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>IJB-C is a datset ...</span></div><div class='hero_subdesc'><span class='bgpad'>The IJB-C dataset contains... +</span></div></div></section><section><h2>Brainwash Dataset</h2> +</section><section><div class='right-sidebar'><div class='meta'> + <div class='gray'>Published</div> + <div>2017</div> + </div><div class='meta'> + <div class='gray'>Images</div> + <div>21,294 </div> + </div><div class='meta'> + <div class='gray'>Videos</div> + <div>11,779 </div> + </div><div class='meta'> + <div class='gray'>Identities</div> + <div>3,531 </div> + </div><div class='meta'> + <div class='gray'>Purpose</div> + <div>face recognition challenge by NIST in full motion videos</div> + </div><div class='meta'> + <div class='gray'>Website</div> + <div><a href='https://www.nist.gov/programs-projects/face-challenges' target='_blank' rel='nofollow noopener'>nist.gov</a></div> + </div></div><p>Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,918 images of "everyday life of a busy downtown cafe"<a class="footnote_shim" name="[^readme]_1"> </a><a href="#[^readme]" class="footnote" title="Footnote 1">1</a> captured at 100 second intervals throught the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's <a href="https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666">reserach paper</a> introducing the dataset, the images were acquired with the help of Angelcam.com<a class="footnote_shim" name="[^end_to_end]_1"> </a><a href="#[^end_to_end]" class="footnote" title="Footnote 2">2</a></p> +<p>The Brainwash dataset is unique because it uses images from a publicly available webcam that records people inside a privately owned business without any consent. No ordinary cafe custom could ever suspect there image would end up in dataset used for surveillance reserach and development, but that is exactly what happened to customers at Brainwash cafe in San Francisco.</p> +<p>Although Brainwash appears to be a less popular dataset, it was used in 2016 and 2017 by researchers from the National University of Defense Technology in China took note of the dataset and used it for two <a href="https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c">research</a> <a href="https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b">projects</a> on advancing the capabilities of object detection to more accurately isolate the target region in an image (<a href="https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_05006.pdf">PDF</a>). <a class="footnote_shim" name="[^localized_region_context]_1"> </a><a href="#[^localized_region_context]" class="footnote" title="Footnote 3">3</a> <a class="footnote_shim" name="[^replacement_algorithm]_1"> </a><a href="#[^replacement_algorithm]" class="footnote" title="Footnote 4">4</a>. The dataset also appears in a 2017 <a href="https://ieeexplore.ieee.org/document/7877809">research paper</a> from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes".</p> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_grid.jpg' alt=' A visualization of 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> A visualization of 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section> + <h3>Who used IJB-C?</h3> + + <p> + This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. + </p> + + </section> + +<section class="applet_container"> +<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span> +</div> --> + <div class="applet" data-payload="{"command": "chart"}"></div> +</section> + +<section class="applet_container"> + <div class="applet" data-payload="{"command": "piechart"}"></div> +</section> + +<section> + + <h3>Biometric Trade Routes</h3> + + <p> + To help understand how IJB-C has been used around the world by commercial, military, and academic organizations; existing publicly available research citing IARPA Janus Benchmark C was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. + </p> + + </section> + +<section class="applet_container fullwidth"> + <div class="applet" data-payload="{"command": "map"}"></div> +</section> + +<div class="caption"> + <ul class="map-legend"> + <li class="edu">Academic</li> + <li class="com">Commercial</li> + <li class="gov">Military / Government</li> + </ul> + <div class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</div > +</div> + + +<section class="applet_container"> + + <h3>Dataset Citations</h3> + <p> + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. + </p> + + <div class="applet" data-payload="{"command": "citations"}"></div> +</section><section> + + <div class="hr-wave-holder"> + <div class="hr-wave-line hr-wave-line1"></div> + <div class="hr-wave-line hr-wave-line2"></div> + </div> + + <h2>Supplementary Information</h2> + +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_example.jpg' alt=' An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains 11,916 more images like this one. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains 11,916 more images like this one. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_saliency_map.jpg' alt=' A visualization of the active regions for 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> A visualization of the active regions for 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section> + + <h4>Cite Our Work</h4> + <p> + + If you use our data, research, or graphics please cite our work: + +<pre id="cite-bibtex"> +@online{megapixels, + author = {Harvey, Adam. LaPlace, Jules.}, + title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, + year = 2019, + url = {https://megapixels.cc/}, + urldate = {2019-04-18} +}</pre> + + </p> +</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^readme]" class="footnote_shim"></a><span class="backlinks"><a href="#[^readme]_1">a</a></span><p>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>.</p> +</li><li><a name="[^end_to_end]" class="footnote_shim"></a><span class="backlinks"><a href="#[^end_to_end]_1">a</a></span><p>Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.</p> +</li><li><a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"><a href="#[^localized_region_context]_1">a</a></span><p>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.</p> +</li><li><a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"><a href="#[^replacement_algorithm]_1">a</a></span><p>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.</p> +</li></ul></section></section> + + </div> + <footer> + <ul class="footer-left"> + <li><a href="/">MegaPixels.cc</a></li> + <li><a href="/datasets/">Datasets</a></li> + <li><a href="/about/">About</a></li> + <li><a href="/about/press/">Press</a></li> + <li><a href="/about/legal/">Legal and Privacy</a></li> + </ul> + <ul class="footer-right"> + <li>MegaPixels ©2017-19 <a href="https://ahprojects.com">Adam R. Harvey</a></li> + <li>Made with support from <a href="https://mozilla.org">Mozilla</a></li> + </ul> + </footer> +</body> + +<script src="/assets/js/dist/index.js"></script> +</html>
\ No newline at end of file diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 0f782924..cf1f5e5e 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -52,7 +52,7 @@ </div></div><p>Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,918 images of "everyday life of a busy downtown cafe"<a class="footnote_shim" name="[^readme]_1"> </a><a href="#[^readme]" class="footnote" title="Footnote 1">1</a> captured at 100 second intervals throught the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's <a href="https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666">reserach paper</a> introducing the dataset, the images were acquired with the help of Angelcam.com<a class="footnote_shim" name="[^end_to_end]_1"> </a><a href="#[^end_to_end]" class="footnote" title="Footnote 2">2</a></p> <p>The Brainwash dataset is unique because it uses images from a publicly available webcam that records people inside a privately owned business without any consent. No ordinary cafe custom could ever suspect there image would end up in dataset used for surveillance reserach and development, but that is exactly what happened to customers at Brainwash cafe in San Francisco.</p> <p>Although Brainwash appears to be a less popular dataset, it was used in 2016 and 2017 by researchers from the National University of Defense Technology in China took note of the dataset and used it for two <a href="https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c">research</a> <a href="https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b">projects</a> on advancing the capabilities of object detection to more accurately isolate the target region in an image (<a href="https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_05006.pdf">PDF</a>). <a class="footnote_shim" name="[^localized_region_context]_1"> </a><a href="#[^localized_region_context]" class="footnote" title="Footnote 3">3</a> <a class="footnote_shim" name="[^replacement_algorithm]_1"> </a><a href="#[^replacement_algorithm]" class="footnote" title="Footnote 4">4</a>. The dataset also appears in a 2017 <a href="https://ieeexplore.ieee.org/document/7877809">research paper</a> from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes".</p> -</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_grid.jpg' alt=' A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc'><div class='caption'> A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc</div></div></section><section> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_grid.jpg' alt=' A visualization of 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> A visualization of 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section> <h3>Who used Brainwash Dataset?</h3> <p> @@ -99,7 +99,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -112,7 +112,7 @@ <h2>Supplementary Information</h2> -</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_example.jpg' alt=' An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_saliency_map.jpg' alt=' A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc'><div class='caption'> A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc</div></div></section><section> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_example.jpg' alt=' An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains 11,916 more images like this one. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains 11,916 more images like this one. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_saliency_map.jpg' alt=' A visualization of the active regions for 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> A visualization of the active regions for 81,973 head annotations from the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section> <h4>Cite Our Work</h4> <p> @@ -129,10 +129,10 @@ }</pre> </p> -</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^readme]" class="footnote_shim"></a><span class="backlinks"><a href="#[^readme]_1">a</a></span><p>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>.</p> -</li><li><a name="[^end_to_end]" class="footnote_shim"></a><span class="backlinks"><a href="#[^end_to_end]_1">a</a></span><p>Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.</p> -</li><li><a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"><a href="#[^localized_region_context]_1">a</a></span><p>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.</p> -</li><li><a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"><a href="#[^replacement_algorithm]_1">a</a></span><p>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.</p> +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^readme]" class="footnote_shim"></a><span class="backlinks"><a href="#[^readme]_1">a</a></span>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>. +</li><li>2 <a name="[^end_to_end]" class="footnote_shim"></a><span class="backlinks"><a href="#[^end_to_end]_1">a</a></span>Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. +</li><li>3 <a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"><a href="#[^localized_region_context]_1">a</a></span>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. +</li><li>4 <a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"><a href="#[^replacement_algorithm]_1">a</a></span>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering. </li></ul></section></section> </div> diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html index abb55148..e86c5ca3 100644 --- a/site/public/datasets/caltech_10k/index.html +++ b/site/public/datasets/caltech_10k/index.html @@ -96,7 +96,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html index a4a7efa2..0236b91c 100644 --- a/site/public/datasets/celeba/index.html +++ b/site/public/datasets/celeba/index.html @@ -94,7 +94,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html index c6d7417e..b0e73dac 100644 --- a/site/public/datasets/cofw/index.html +++ b/site/public/datasets/cofw/index.html @@ -87,7 +87,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -138,7 +138,7 @@ To increase the number of training images, and since COFW has the exact same la <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 3c0bc0c2..90c131b8 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -48,11 +48,7 @@ <div><a href='http://vision.cs.duke.edu/DukeMTMC/' target='_blank' rel='nofollow noopener'>duke.edu</a></div> </div></div><p>Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy"<a class="footnote_shim" name="[^duke_mtmc_orig]_1"> </a><a href="#[^duke_mtmc_orig]" class="footnote" title="Footnote 1">1</a>.</p> <p>In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.</p> -<<<<<<< HEAD -<p>In one 2018 <a href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf">paper</a> jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled <a href="https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e">Attention-Aware Compositional Network for Person Re-identification</a>, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. <a class="footnote_shim" name="[^sensetime_qz]_1"> </a><a href="#[^sensetime_qz]" class="footnote" title="Footnote 2">2</a><a class="footnote_shim" name="[^sensenets_uyghurs]_1"> </a><a href="#[^sensenets_uyghurs]" class="footnote" title="Footnote 3">3</a><a class="footnote_shim" name="[^xinjiang_nyt]_1"> </a><a href="#[^xinjiang_nyt]" class="footnote" title="Footnote 4">4</a></p> -======= -<p>In one 2018 <a href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf">paper</a> jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled <a href="https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e">Attention-Aware Compositional Network for Person Re-identification</a>, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. <a class="footnote_shim" name="[^xinjiang_nyt]_1"> </a><a href="#[^xinjiang_nyt]" class="footnote" title="Footnote 1">1</a><a class="footnote_shim" name="[^sensetime_qz]_1"> </a><a href="#[^sensetime_qz]" class="footnote" title="Footnote 2">2</a><a class="footnote_shim" name="[^sensenets_uyghurs]_1"> </a><a href="#[^sensenets_uyghurs]" class="footnote" title="Footnote 3">3</a></p> ->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 +<p>In one 2018 <a href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf">paper</a> jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled <a href="https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e">Attention-Aware Compositional Network for Person Re-identification</a>, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. <a class="footnote_shim" name="[^xinjiang_nyt]_1"> </a><a href="#[^xinjiang_nyt]" class="footnote" title="Footnote 4">4</a><a class="footnote_shim" name="[^sensetime_qz]_1"> </a><a href="#[^sensetime_qz]" class="footnote" title="Footnote 2">2</a><a class="footnote_shim" name="[^sensenets_uyghurs]_1"> </a><a href="#[^sensenets_uyghurs]" class="footnote" title="Footnote 3">3</a></p> </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_reid_montage.jpg' alt=' A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.'><div class='caption'> A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.</div></div></section><section><p>Despite <a href="https://www.hrw.org/news/2017/11/19/china-police-big-data-systems-violate-privacy-target-dissent">repeated</a> <a href="https://www.hrw.org/news/2018/02/26/china-big-data-fuels-crackdown-minority-region">warnings</a> by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology.</p> <table> <thead><tr> @@ -200,15 +196,9 @@ </tbody> </table> <p>By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original <a href="http://vision.cs.duke.edu/DukeMTMC/">intent</a> of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".</p> -<<<<<<< HEAD <p>The same logic applies for all the new extensions of the Duke MTMC dataset including <a href="https://github.com/layumi/DukeMTMC-reID_evaluation">Duke MTMC Re-ID</a>, <a href="https://github.com/Yu-Wu/DukeMTMC-VideoReID">Duke MTMC Video Re-ID</a>, Duke MTMC Groups, and <a href="https://github.com/vana77/DukeMTMC-attribute">Duke MTMC Attribute</a>. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called <a href="https://qmul-survface.github.io/">QMUL-SurvFace</a>, which was funded in part by <a href="https://seequestor.com">SeeQuestor</a>, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it, so long as it advances their own interests in artificial intelligence.</p> </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliencies.jpg' alt=' Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc'><div class='caption'> Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc</div></div></section><section><p>But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against your own political interests, or against universal human rights.</p> -<p>For the approximately 2,000 students in Duke MTMC dataset, there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.</p> -======= -<p>The same logic applies for all the new extensions of the Duke MTMC dataset including <a href="https://github.com/layumi/DukeMTMC-reID_evaluation">Duke MTMC Re-ID</a>, <a href="https://github.com/Yu-Wu/DukeMTMC-VideoReID">Duke MTMC Video Re-ID</a>, Duke MTMC Groups, and <a href="https://github.com/vana77/DukeMTMC-attribute">Duke MTMC Attribute</a>. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called <a href="https://qmul-survface.github.io/">QMUL-SurvFace</a>, which was funded in part by <a href="https://seequestor.com">SeeQuestor</a>, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence.</p> -</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliencies.jpg' alt=' Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc'><div class='caption'> Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc</div></div></section><section><p>But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests.</p> <p>For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class in 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.</p> ->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_cameras.jpg' alt=' Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc'><div class='caption'> Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_camera_map.jpg' alt=' Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.'><div class='caption'> Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.</div></div></section><section> <h3>Who used Duke MTMC Dataset?</h3> @@ -256,7 +246,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -270,11 +260,7 @@ <h2>Supplementary Information</h2> </section><section><h4>Video Timestamps</h4> -<<<<<<< HEAD -<p>The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop <a href="http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync">time sync data</a> provided by the researchers, it at least aligns the relative time. The <a href="https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999">rainy weather</a> on that day also contributes towards the likelihood of March 14, 2014.</p> -======= <p>The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop <a href="http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync">time sync data</a> provided by the researchers, it at least confirms the relative timing. The <a href="https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999">precipitous weather</a> on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date.</p> ->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 </section><section><div class='columns columns-2'><div class='column'><table> <thead><tr> <th>Camera</th> @@ -345,28 +331,11 @@ </tr> </tbody> </table> -<<<<<<< HEAD </div></div></section><section><h4>Errata</h4> -<ul> -<li>The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812.</li> -</ul> -<h4>Citing Duke MTMC</h4> -<p>If you use any data from the Duke MTMC, please follow their <a href="http://vision.cs.duke.edu/DukeMTMC/#how-to-cite">license</a> and cite their work as:</p> -<pre> -@inproceedings{ristani2016MTMC, - title = {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking}, - author = {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo}, - booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking}, - year = {2016} -} -</pre></section><section> -======= -</div></div></section><section><h4>Notes</h4> -<p>The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students.</p> +<p>The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this writeup we used 2,000 to describe the approximate number of students.</p> <h4>Ethics</h4> <p>Please direct any questions about the ethics of the dataset to Duke University's <a href="https://hr.duke.edu/policies/expectations/compliance/">Institutional Ethics & Compliance Office</a> using the number at the bottom of the page.</p> </section><section> ->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 <h4>Cite Our Work</h4> <p> @@ -383,17 +352,8 @@ }</pre> </p> -<<<<<<< HEAD -</section><section><h4>ToDo</h4> -<ul> -<li>clean up citations, formatting</li> -</ul> -</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^duke_mtmc_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^duke_mtmc_orig]_1">a</a></span>"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. <a href="https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c">SemanticScholar</a> -</li><li>2 <a name="[^sensetime_qz]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime_qz]_1">a</a></span><a href="https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/">https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/</a> -</li><li>3 <a name="[^sensenets_uyghurs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensenets_uyghurs]_1">a</a></span><a href="https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/">https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/</a> -</li><li>4 <a name="[^xinjiang_nyt]" class="footnote_shim"></a><span class="backlinks"><a href="#[^xinjiang_nyt]_1">a</a></span>Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html</a>. April 14, 2019. -======= -</section><section><p>If you use any data from the Duke MTMC please follow their <a href="http://vision.cs.duke.edu/DukeMTMC/#how-to-cite">license</a> and cite their work as:</p> +</section><section><h4>Citing Duke MTMC</h4> +<p>If you use any data from the Duke MTMC, please follow their <a href="http://vision.cs.duke.edu/DukeMTMC/#how-to-cite">license</a> and cite their work as:</p> <pre> @inproceedings{ristani2016MTMC, title = {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking}, @@ -401,26 +361,25 @@ booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking}, year = {2016} } -</pre></section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^xinjiang_nyt]" class="footnote_shim"></a><span class="backlinks"><a href="#[^xinjiang_nyt]_1">a</a></span><p>Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html</a>. April 14, 2019.</p> -</li><li><a name="[^sensetime_qz]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime_qz]_1">a</a></span><p><a href="https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/">https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/</a></p> -</li><li><a name="[^sensenets_uyghurs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensenets_uyghurs]_1">a</a></span><p><a href="https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/">https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/</a></p> -</li><li><a name="[^duke_mtmc_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^duke_mtmc_orig]_1">a</a></span><p>"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. <a href="https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c">SemanticScholar</a></p> ->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 +</pre></section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^duke_mtmc_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^duke_mtmc_orig]_1">a</a></span>"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. <a href="https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c">SemanticScholar</a> +</li><li>2 <a name="[^sensetime_qz]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime_qz]_1">a</a></span><a href="https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/">https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/</a> +</li><li>3 <a name="[^sensenets_uyghurs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensenets_uyghurs]_1">a</a></span><a href="https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/">https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/</a> +</li><li>4 <a name="[^xinjiang_nyt]" class="footnote_shim"></a><span class="backlinks"><a href="#[^xinjiang_nyt]_1">a</a></span>Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html</a>. April 14, 2019. </li></ul></section></section> </div> <footer> - <div> - <a href="/">MegaPixels.cc</a> - <a href="/datasets/">Datasets</a> - <a href="/about/">About</a> - <a href="/about/press/">Press</a> - <a href="/about/legal/">Legal and Privacy</a> - </div> - <div> - MegaPixels ©2017-19 Adam R. Harvey / - <a href="https://ahprojects.com">ahprojects.com</a> - </div> + <ul class="footer-left"> + <li><a href="/">MegaPixels.cc</a></li> + <li><a href="/datasets/">Datasets</a></li> + <li><a href="/about/">About</a></li> + <li><a href="/about/press/">Press</a></li> + <li><a href="/about/legal/">Legal and Privacy</a></li> + </ul> + <ul class="footer-right"> + <li>MegaPixels ©2017-19 <a href="https://ahprojects.com">Adam R. Harvey</a></li> + <li>Made with support from <a href="https://mozilla.org">Mozilla</a></li> + </ul> </footer> </body> diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html index 7f9ed94c..09abaee2 100644 --- a/site/public/datasets/feret/index.html +++ b/site/public/datasets/feret/index.html @@ -90,7 +90,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/ijb_c/index.html b/site/public/datasets/ijb_c/index.html new file mode 100644 index 00000000..b6a16bfe --- /dev/null +++ b/site/public/datasets/ijb_c/index.html @@ -0,0 +1,151 @@ +<!doctype html> +<html> +<head> + <title>MegaPixels</title> + <meta charset="utf-8" /> + <meta name="author" content="Adam Harvey" /> + <meta name="description" content="IJB-C is a datset ..." /> + <meta name="referrer" content="no-referrer" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> + <link rel='stylesheet' href='/assets/css/fonts.css' /> + <link rel='stylesheet' href='/assets/css/css.css' /> + <link rel='stylesheet' href='/assets/css/leaflet.css' /> + <link rel='stylesheet' href='/assets/css/applets.css' /> +</head> +<body> + <header> + <a class='slogan' href="/"> + <div class='logo'></div> + <div class='site_name'>MegaPixels</div> + <div class='splash'>IJB-C</div> + </a> + <div class='links'> + <a href="/datasets/">Datasets</a> + <a href="/about/">About</a> + </div> + </header> + <div class="content content-dataset"> + + <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>IJB-C is a datset ...</span></div><div class='hero_subdesc'><span class='bgpad'>The IJB-C dataset contains... +</span></div></div></section><section><h2>IARPA Janus Benchmark C (IJB-C)</h2> +</section><section><div class='right-sidebar'><div class='meta'> + <div class='gray'>Published</div> + <div>2017</div> + </div><div class='meta'> + <div class='gray'>Images</div> + <div>21,294 </div> + </div><div class='meta'> + <div class='gray'>Videos</div> + <div>11,779 </div> + </div><div class='meta'> + <div class='gray'>Identities</div> + <div>3,531 </div> + </div><div class='meta'> + <div class='gray'>Purpose</div> + <div>face recognition challenge by NIST in full motion videos</div> + </div><div class='meta'> + <div class='gray'>Website</div> + <div><a href='https://www.nist.gov/programs-projects/face-challenges' target='_blank' rel='nofollow noopener'>nist.gov</a></div> + </div></div><p>Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor</p> +<p>Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor</p> +</section><section> + <h3>Who used IJB-C?</h3> + + <p> + This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. + </p> + + </section> + +<section class="applet_container"> +<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span> +</div> --> + <div class="applet" data-payload="{"command": "chart"}"></div> +</section> + +<section class="applet_container"> + <div class="applet" data-payload="{"command": "piechart"}"></div> +</section> + +<section> + + <h3>Biometric Trade Routes</h3> + + <p> + To help understand how IJB-C has been used around the world by commercial, military, and academic organizations; existing publicly available research citing IARPA Janus Benchmark C was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. + </p> + + </section> + +<section class="applet_container fullwidth"> + <div class="applet" data-payload="{"command": "map"}"></div> +</section> + +<div class="caption"> + <ul class="map-legend"> + <li class="edu">Academic</li> + <li class="com">Commercial</li> + <li class="gov">Military / Government</li> + </ul> + <div class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</div > +</div> + + +<section class="applet_container"> + + <h3>Dataset Citations</h3> + <p> + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. + </p> + + <div class="applet" data-payload="{"command": "citations"}"></div> +</section><section> + + <div class="hr-wave-holder"> + <div class="hr-wave-line hr-wave-line1"></div> + <div class="hr-wave-line hr-wave-line2"></div> + </div> + + <h2>Supplementary Information</h2> + +</section><section> + + <h4>Cite Our Work</h4> + <p> + + If you use our data, research, or graphics please cite our work: + +<pre id="cite-bibtex"> +@online{megapixels, + author = {Harvey, Adam. LaPlace, Jules.}, + title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, + year = 2019, + url = {https://megapixels.cc/}, + urldate = {2019-04-18} +}</pre> + + </p> +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^readme]" class="footnote_shim"></a><span class="backlinks"></span>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>. +</li><li>2 <a name="[^end_to_end]" class="footnote_shim"></a><span class="backlinks"></span>Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. +</li><li>3 <a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"></span>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. +</li><li>4 <a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"></span>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering. +</li></ul></section></section> + + </div> + <footer> + <ul class="footer-left"> + <li><a href="/">MegaPixels.cc</a></li> + <li><a href="/datasets/">Datasets</a></li> + <li><a href="/about/">About</a></li> + <li><a href="/about/press/">Press</a></li> + <li><a href="/about/legal/">Legal and Privacy</a></li> + </ul> + <ul class="footer-right"> + <li>MegaPixels ©2017-19 <a href="https://ahprojects.com">Adam R. Harvey</a></li> + <li>Made with support from <a href="https://mozilla.org">Mozilla</a></li> + </ul> + </footer> +</body> + +<script src="/assets/js/dist/index.js"></script> +</html>
\ No newline at end of file diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index 5c8e2546..c6c4185a 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -73,6 +73,18 @@ </div> </a> + <a href="/datasets/ijb_c/" style="background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/index.jpg)"> + <div class="dataset"> + <span class='title'>IJB-C</span> + <div class='fields'> + <div class='year visible'><span>2017</span></div> + <div class='purpose'><span>face recognition challenge by NIST in full motion videos</span></div> + <div class='images'><span>21,294 images</span></div> + <div class='identities'><span>3,531 </span></div> + </div> + </div> + </a> + <a href="/datasets/msceleb/" style="background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/index.jpg)"> <div class="dataset"> <span class='title'>Microsoft Celeb</span> diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html index a9eb025d..1238c8d3 100644 --- a/site/public/datasets/lfpw/index.html +++ b/site/public/datasets/lfpw/index.html @@ -83,7 +83,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index ff7a3cd9..68021e93 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -97,7 +97,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -141,9 +141,9 @@ <li>The word "future" appears 71 times</li> <li>* denotes partial funding for related research</li> </ul> -</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^lfw_www]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_www]_1">a</a><a href="#[^lfw_www]_2">b</a></span><p><a href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a></p> -</li><li><a name="[^lfw_baidu]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_baidu]_1">a</a></span><p>Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. <a href="https://arxiv.org/abs/1506.07310">https://arxiv.org/abs/1506.07310</a></p> -</li><li><a name="[^lfw_pingan]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_pingan]_1">a</a><a href="#[^lfw_pingan]_2">b</a><a href="#[^lfw_pingan]_3">c</a></span><p>Lee, Justin. "PING AN Tech facial recognition receives high score in latest LFW test results". BiometricUpdate.com. Feb 13, 2017. <a href="https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results">https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results</a></p> +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^lfw_www]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_www]_1">a</a><a href="#[^lfw_www]_2">b</a></span><a href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a> +</li><li>2 <a name="[^lfw_baidu]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_baidu]_1">a</a></span>Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. <a href="https://arxiv.org/abs/1506.07310">https://arxiv.org/abs/1506.07310</a> +</li><li>3 <a name="[^lfw_pingan]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_pingan]_1">a</a><a href="#[^lfw_pingan]_2">b</a><a href="#[^lfw_pingan]_3">c</a></span>Lee, Justin. "PING AN Tech facial recognition receives high score in latest LFW test results". BiometricUpdate.com. Feb 13, 2017. <a href="https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results">https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results</a> </li></ul></section></section> </div> diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html index 05750dc7..a72cb6cf 100644 --- a/site/public/datasets/market_1501/index.html +++ b/site/public/datasets/market_1501/index.html @@ -91,7 +91,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index 86741647..c42a2767 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -4,7 +4,7 @@ <title>MegaPixels</title> <meta charset="utf-8" /> <meta name="author" content="Adam Harvey" /> - <meta name="description" content="MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms" /> + <meta name="description" content="Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition technologies" /> <meta name="referrer" content="no-referrer" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> <link rel='stylesheet' href='/assets/css/fonts.css' /> @@ -26,7 +26,7 @@ </header> <div class="content content-dataset"> - <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms</span></div><div class='hero_subdesc'><span class='bgpad'>The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine + <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition technologies</span></div><div class='hero_subdesc'><span class='bgpad'>The MS Celeb dataset includes over 10 million images of about 100K people and a target list of 1 million individuals </span></div></div></section><section><h2>Microsoft Celeb Dataset (MS Celeb)</h2> </section><section><div class='right-sidebar'><div class='meta'> <div class='gray'>Published</div> @@ -49,8 +49,210 @@ </div><div class='meta'> <div class='gray'>Website</div> <div><a href='http://www.msceleb.org/' target='_blank' rel='nofollow noopener'>msceleb.org</a></div> - </div></div><p><a href="https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology">https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology</a></p> -<p><a href="https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm">https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm</a></p> + </div></div><p>Microsoft Celeb (MS Celeb) is a dataset of 10 million face images scraped from the Internet and used for research and development of large-scale biometric recognition systems. According to Microsoft Research who created and published the <a href="http://msceleb.org">dataset</a> in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of nearly 100,000 individuals. Microsoft's goal in building this dataset was to distribute the initial training dataset of 100,000 individuals images and use this to accelerate reserch into recognizing a target list of one million individuals from their face images "using all the possibly collected face images of this individual on the web as training data".<a class="footnote_shim" name="[^msceleb_orig]_1"> </a><a href="#[^msceleb_orig]" class="footnote" title="Footnote 2">2</a></p> +<p>These one million people, defined as Micrsoft Research as "celebrities", are often merely people who must maintain an online presence for their professional lives. Microsoft's list of 1 million people is an expansive exploitation of the current reality that for many people including academics, policy makers, writers, artists, and especially journalists maintaining an online presence is mandatory and should not allow Microsoft (or anyone else) to use their biometrics for reserach and development of surveillance technology. Many of names in target list even include people critical of the very technology Microsoft is using their name and biometric information to build. The list includes digital rights activists like Jillian York and [add more]; artists critical of surveillance including Trevor Paglen, Hito Steryl, Kyle McDonald, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glen Greenwald; Data and Society founder danah boyd; and even Julie Brill the former FTC commissioner responsible for protecting consumer’s privacy to name a few.</p> +<h3>Microsoft's 1 Million Target List</h3> +<p>Below is a list of names that were included in list of 1 million individuals curated to illustrate Microsoft's expansive and exploitative practice of scraping the Internet for biometric training data. The entire name file can be downloaded from <a href="https://msceleb.org">msceleb.org</a>. Names appearing with * indicate that Microsoft also distributed imaged.</p> +<p>[ cleaning this up ]</p> +</section><section><div class='columns columns-2'><div class='column'><table> +<thead><tr> +<th>Name</th> +<th>ID</th> +<th>Profession</th> +<th>Images</th> +</tr> +</thead> +<tbody> +<tr> +<td>Jeremy Scahill</td> +<td>/m/02p_8_n</td> +<td>Journalist</td> +<td>x</td> +</tr> +<tr> +<td>Jillian York</td> +<td>/m/0g9_3c3</td> +<td>Digital rights activist</td> +<td>x</td> +</tr> +<tr> +<td>Astra Taylor</td> +<td>/m/05f6_39</td> +<td>Author, activist</td> +<td>x</td> +</tr> +<tr> +<td>Jonathan Zittrain</td> +<td>/m/01f75c</td> +<td>EFF board member</td> +<td>no</td> +</tr> +<tr> +<td>Julie Brill</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Jonathan Zittrain</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Bruce Schneier</td> +<td>m.095js</td> +<td>Cryptologist and author</td> +<td>yes</td> +</tr> +<tr> +<td>Julie Brill</td> +<td>m.0bs3s9g</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Kim Zetter</td> +<td>/m/09r4j3</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Ethan Zuckerman</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Jill Magid</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Kyle McDonald</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Trevor Paglen</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>R. Luke DuBois</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +</tbody> +</table> +</div><div class='column'><table> +<thead><tr> +<th>Name</th> +<th>ID</th> +<th>Profession</th> +<th>Images</th> +</tr> +</thead> +<tbody> +<tr> +<td>Trevor Paglen</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Ai Weiwei</td> +<td>/m/0278dyq</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Jer Thorp</td> +<td>/m/01h8lg</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Edward Felten</td> +<td>/m/028_7k</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Evgeny Morozov</td> +<td>/m/05sxhgd</td> +<td>Scholar and technology critic</td> +<td>yes</td> +</tr> +<tr> +<td>danah boyd</td> +<td>/m/06zmx5</td> +<td>Data and Society founder</td> +<td>x</td> +</tr> +<tr> +<td>Bruce Schneier</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Laura Poitras</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Trevor Paglen</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Astra Taylor</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Shoshanaa Zuboff</td> +<td>x</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Eyal Weizman</td> +<td>m.0g54526</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>Aram Bartholl</td> +<td>m.06_wjyc</td> +<td>x</td> +<td>x</td> +</tr> +<tr> +<td>James Risen</td> +<td>m.09pk6b</td> +<td>x</td> +<td>x</td> +</tr> +</tbody> +</table> +</div></div></section><section><p>After publishing this list, researchers from Microsoft Asia then worked with researchers affilliated with China's National University of Defense Technology (controlled by China's Central Military Commission) and used the the MS Celeb dataset for their <a href="https://www.semanticscholar.org/paper/Faces-as-Lighting-Probes-via-Unsupervised-Deep-Yi-Zhu/b301fd2fc33f24d6f75224e7c0991f4f04b64a65">research paper</a> on using "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction" with potential applications in 3D face recognition.</p> +<p>In an article published by the Financial Times based on data discovered during this investigation, Samm Sacks (senior fellow at New American and China tech policy expert) commented that this research raised "red flags because of the nature of the technology, the authors affilliations, combined with the what we know about how this technology is being deployed in China right now".<a class="footnote_shim" name="[^madhu_ft]_1"> </a><a href="#[^madhu_ft]" class="footnote" title="Footnote 3">3</a></p> +<p>Four more papers published by SenseTime which also use the MS Celeb dataset raise similar flags. SenseTime is Beijing based company providing surveillance to Chinese authorities including [ add context here ] has been <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">flagged</a> as complicity in potential human rights violations.</p> +<p>One of the 4 SenseTime papers, "Exploring Disentangled Feature Representation Beyond Face Identification", shows how SenseTime is developing automated face analysis technology to infer race, narrow eyes, nose size, and chin size, all of which could be used to target vulnerable ethnic groups based on their facial appearances.<a class="footnote_shim" name="[^disentangled]_1"> </a><a href="#[^disentangled]" class="footnote" title="Footnote 4">4</a></p> +<p>Earlier in 2019, Microsoft CEO <a href="https://blogs.microsoft.com/on-the-issues/2018/12/06/facial-recognition-its-time-for-action/">Brad Smith</a> called for the governmental regulation of face recognition, citing the potential for misuse, a rare admission that Microsoft's surveillance-driven business model had lost its bearing. More recently Smith also <a href="https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV">announced</a> that Microsoft would seemingly take stand against potential misuse and decided to not sell face recognition to an unnamed United States law enforcement agency, citing that their technology was not accurate enough to be used on minorities because it was trained mostly on white male faces.</p> +<p>What the decision to block the sale announces is not so much that Microsoft has upgraded their ethics, but that it publicly acknolwedged it can't sell a data-driven product without data. Microsoft can't sell face recognition for faces they can't train on.</p> +<p>Until now, that data has been freely harvested from the Internet and packaged in training sets like MS Celeb, which are overwhelmingly <a href="https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html">white</a> and <a href="https://gendershades.org">male</a>. Without balanced data, facial recognition contains blind spots. And without datasets like MS Celeb, the powerful yet innaccurate facial recognition services like Microsoft's Azure Cognitive Service also would not be able to see at all.</p> +<p>Microsoft didn't only create MS Celeb for other researchers to use, they also used it internally. In a publicly available 2017 Microsoft Research project called "(<a href="https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/">One-shot Face Recognition by Promoting Underrepresented Classes</a>)", Microsoft leveraged the MS Celeb dataset to analyse their algorithms and advertise the results. Interestingly, the Microsoft's <a href="https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/">corporate version</a> does not mention they used the MS Celeb datset, but the <a href="https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70">open-acess version</a> of the paper published on arxiv.org that same year explicity mentions that Microsoft Research tested their algorithms "on the MS-Celeb-1M low-shot learning benchmark task."</p> +<p>We suggest that if Microsoft Research wants biometric data for surveillance research and development, they should start with own researcher's biometric data instead of scraping the Internet for journalists, artists, writers, and academics.</p> </section><section> <h3>Who used Microsoft Celeb?</h3> @@ -98,7 +300,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -111,13 +313,10 @@ <h2>Supplementary Information</h2> -</section><section><h3>Additional Information</h3> -<ul> -<li>The dataset author spoke about his research at the CVPR conference in 2016 <a href="https://www.youtube.com/watch?v=Nl2fBKxwusQ">https://www.youtube.com/watch?v=Nl2fBKxwusQ</a></li> -</ul> -</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^readme]" class="footnote_shim"></a><span class="backlinks"></span><p>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>.</p> -</li><li><a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"></span><p>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.</p> -</li><li><a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"></span><p>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.</p> +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^brad_smith]" class="footnote_shim"></a><span class="backlinks"></span>Brad Smith cite +</li><li>2 <a name="[^msceleb_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^msceleb_orig]_1">a</a></span>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition +</li><li>3 <a name="[^madhu_ft]" class="footnote_shim"></a><span class="backlinks"><a href="#[^madhu_ft]_1">a</a></span>Microsoft worked with Chinese military university on artificial intelligence +</li><li>4 <a name="[^disentangled]" class="footnote_shim"></a><span class="backlinks"><a href="#[^disentangled]_1">a</a></span>"Exploring Disentangled Feature Representation Beyond Face Identification" </li></ul></section></section> </div> diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index 03d8934b..fabcae6b 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -49,7 +49,7 @@ </div><div class='meta'> <div class='gray'>Website</div> <div><a href='http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html' target='_blank' rel='nofollow noopener'>ox.ac.uk</a></div> - </div></div><p>The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.<a class="footnote_shim" name="[^ben_benfold_orig]_1"> </a><a href="#[^ben_benfold_orig]" class="footnote" title="Footnote 1">1</a> The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009<a class="footnote_shim" name="[^guiding_surveillance]_1"> </a><a href="#[^guiding_surveillance]" class="footnote" title="Footnote 2">2</a> the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more.</p> + </div></div><p>The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.<a class="footnote_shim" name="[^ben_benfold_orig]_1"> </a><a href="#[^ben_benfold_orig]" class="footnote" title="Footnote 1">1</a> The CCTV video was obtained from a surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009<a class="footnote_shim" name="[^guiding_surveillance]_1"> </a><a href="#[^guiding_surveillance]" class="footnote" title="Footnote 2">2</a> the <a href="http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html">Oxford Town Centre dataset</a> has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more.</p> <p>The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project.</p> </section><section> <h3>Who used TownCentre?</h3> @@ -98,7 +98,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -112,8 +112,8 @@ <h2>Supplementary Information</h2> </section><section><h3>Location</h3> -<p>The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs <a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">source</a>. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their <a href="http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html">research projects</a> it is likely that they would also be able to access to this camera.</p> -<p>To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera <a href="https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/">pointing in the same direction</a> as the Oxford Town Centre dataset proving the camera can and has been rotated before.</p> +<p>The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs <a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">source</a>. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their <a href="http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html">research projects</a> it is increasingly likely that they would also be able to access to this camera.</p> +<p>Next, to discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera <a href="https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/">pointing in the same direction</a> as the Oxford Town Centre dataset proving the camera can and has been rotated before.</p> <p>As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse (<a href="https://www.flickr.com/photos/katieportwin/364492063/in/photolist-4meWFE-yd7rw-yd7X6-5sDHuc-yd7DN-59CpEK-5GoHAc-yd7Zh-3G2uJP-yd7US-5GomQH-4peYpq-4bAEwm-PALEr-58RkAp-5pHEkf-5v7fGq-4q1J9W-4kypQ2-5KX2Eu-yd7MV-yd7p6-4McgWb-5pJ55w-24N9gj-37u9LK-4FVcKQ-a81Enz-5qNhTG-59CrMZ-2yuwYM-5oagH5-59CdsP-4FVcKN-4PdxhC-5Lhr2j-2PAd2d-5hAwvk-zsQSG-4Cdr4F-3dUPEi-9B1RZ6-2hv5NY-4G5qwP-HCHBW-4JiuC4-4Pdr9Y-584aEV-2GYBEc-HCPkp/">photo</a>, <a href="http://www.oxfordhistory.org.uk/cornmarket/west/47_51.html">history</a>) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a <a href="web.archive.org/web/20081201002524/http://www.gap.com/">GAP website snapshot</a> from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal.</p> </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_cctv.jpg' alt=' Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)'><div class='caption'> Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)</div></div></section><section><div class='columns columns-'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_sal_body.jpg' alt=' Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc'><div class='caption'> Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_sal_face.jpg' alt=' Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc'><div class='caption'> Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc</div></div></section></div></section><section> @@ -132,8 +132,8 @@ }</pre> </p> -</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^ben_benfold_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^ben_benfold_orig]_1">a</a></span><p>Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.</p> -</li><li><a name="[^guiding_surveillance]" class="footnote_shim"></a><span class="backlinks"><a href="#[^guiding_surveillance]_1">a</a></span><p>"Guiding Visual Surveillance by Tracking Human Attention". 2009.</p> +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <a name="[^ben_benfold_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^ben_benfold_orig]_1">a</a></span>Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464. +</li><li>2 <a name="[^guiding_surveillance]" class="footnote_shim"></a><span class="backlinks"><a href="#[^guiding_surveillance]_1">a</a></span>"Guiding Visual Surveillance by Tracking Human Attention". 2009. </li></ul></section></section> </div> diff --git a/site/public/datasets/pipa/index.html b/site/public/datasets/pipa/index.html index ae8aef6d..297f4d45 100644 --- a/site/public/datasets/pipa/index.html +++ b/site/public/datasets/pipa/index.html @@ -94,7 +94,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html index ef289954..5feed748 100644 --- a/site/public/datasets/pubfig/index.html +++ b/site/public/datasets/pubfig/index.html @@ -91,7 +91,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 3652e329..3296cabc 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -51,7 +51,7 @@ <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 – 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 UnContsrained 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> +</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. '><div class='caption'> Example images from the UnConstrained College Students Dataset. </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 © megapixels.cc'><div class='caption'> UCCS photos captured per weekday © megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_exif_plot.png' alt=' UCCS photos captured per weekday © megapixels.cc'><div class='caption'> UCCS photos captured per weekday © megapixels.cc</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> @@ -104,7 +104,7 @@ Their setup made it impossible for students to know they were being photographed <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> @@ -250,10 +250,10 @@ Their setup made it impossible for students to know they were being photographed }</pre> </p> -</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^uccs_vast]" class="footnote_shim"></a><span class="backlinks"><a href="#[^uccs_vast]_1">a</a></span><p>"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.</p> -</li><li><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><p>Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.</p> -</li><li><a name="[^funding_uccs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^funding_uccs]_1">a</a></span><p>Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.</p> -</li><li><a name="[^surv_face_qmul]" class="footnote_shim"></a><span class="backlinks"><a href="#[^surv_face_qmul]_1">a</a></span><p>"Surveillance Face Recognition Challenge". <a href="https://www.semanticscholar.org/paper/Surveillance-Face-Recognition-Challenge-Cheng-Zhu/2306b2a8fba28539306052764a77a0d0f5d1236a">SemanticScholar</a></p> +</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></ul></section></section> </div> diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 24ce4b2d..5f314d9e 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -96,7 +96,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/viper/index.html b/site/public/datasets/viper/index.html index e4b2a05a..4d2abbe1 100644 --- a/site/public/datasets/viper/index.html +++ b/site/public/datasets/viper/index.html @@ -96,7 +96,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html index e90b45cb..d0a7a172 100644 --- a/site/public/datasets/youtube_celebrities/index.html +++ b/site/public/datasets/youtube_celebrities/index.html @@ -75,7 +75,7 @@ <h3>Dataset Citations</h3> <p> - The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. + The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>. </p> <div class="applet" data-payload="{"command": "citations"}"></div> diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 535958cc..ef8a5316 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -42,10 +42,15 @@ </section> <section><div class='meta'><div><div class='gray'>Posted</div><div>Dec. 15</div></div><div><div class='gray'>Author</div><div>Adam Harvey</div></div></div><section><section><p>Facial recognition is a scam.</p> +<p>It's extractive and damaging industry that's built on the biometric backbone of the Internet.</p> <p>During the last 20 years commericial, academic, and governmental agencies have promoted the false dream of a future with face recognition. This essay debunks the popular myth that such a thing ever existed.</p> <p>There is no such thing as <em>face recognition</em>. For the last 20 years, government agencies, commercial organizations, and academic institutions have played the public as a fool, selling a roadmap of the future that simply does not exist. Facial recognition, as it is currently defined, promoted, and sold to the public, government, and commercial sector is a scam.</p> <p>Committed to developing robust solutions with superhuman accuracy, the industry has repeatedly undermined itself by never actually developing anything close to "face recognition".</p> <p>There is only biased feature vector clustering and probabilistic thresholding.</p> +<h2>If you don't have data, you don't have a product.</h2> +<p>Yesterday's <a href="https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV">decision</a> by Brad Smith, CEO of Microsoft, to not sell facial recognition to a US law enforcement agency is not an about face by Microsoft to become more humane, it's simply a perfect illustration of the value of training data. Without data, you don't have a product to sell. Microsoft realized that doesn't have enough training data to sell</p> +<h2>Use Your Own Biometrics First</h2> +<p>If researchers want faces, they should take selfies and create their own dataset. If researchers want images of families to build surveillance software, they should use and distibute their own family portraits.</p> <h3>Motivation</h3> <p>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>.</p> <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.</p> diff --git a/site/public/research/02_what_computers_can_see/index.html b/site/public/research/02_what_computers_can_see/index.html index d139e83e..aac0b723 100644 --- a/site/public/research/02_what_computers_can_see/index.html +++ b/site/public/research/02_what_computers_can_see/index.html @@ -52,6 +52,10 @@ <li>tired, drowsiness in car</li> <li>affectiva: interest in product, intent to buy</li> </ul> +<h2>From SenseTime paper</h2> +<p>Exploring Disentangled Feature Representation Beyond Face Identification</p> +<p>From <a href="https://arxiv.org/pdf/1804.03487.pdf">https://arxiv.org/pdf/1804.03487.pdf</a> +The attribute IDs from 1 to 40 corre-spond to: ‘5 o Clock Shadow’, ‘Arched Eyebrows’, ‘Attrac-tive’, ‘Bags Under Eyes’, ‘Bald’, ‘Bangs’, ‘Big Lips’, ‘BigNose’, ‘Black Hair’, ‘Blond Hair’, ‘Blurry’, ‘Brown Hair’,‘Bushy Eyebrows’, ‘Chubby’, ‘Double Chin’, ‘Eyeglasses’,‘Goatee’, ‘Gray Hair’, ‘Heavy Makeup’, ‘High Cheek-bones’, ‘Male’, ‘Mouth Slightly Open’, ‘Mustache’, ‘Nar-row Eyes’, ‘No Beard’, ‘Oval Face’, ‘Pale Skin’, ‘PointyNose’, ‘Receding Hairline’, ‘Rosy Cheeks’, ‘Sideburns’,‘Smiling’, ‘Straight Hair’, ‘Wavy Hair’, ‘Wearing Ear-rings’, ‘Wearing Hat’, ‘Wearing Lipstick’, ‘Wearing Neck-lace’, ‘Wearing Necktie’ and ‘Young’. It’</p> <h2>From PubFig Dataset</h2> <ul> <li>Male</li> |