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Diffstat (limited to 'site/content')
| -rw-r--r-- | site/content/pages/about/attribution.md | 2 | ||||
| -rw-r--r-- | site/content/pages/about/index.md | 13 | ||||
| -rw-r--r-- | site/content/pages/datasets/brainwash/index.md | 14 | ||||
| -rw-r--r-- | site/content/pages/datasets/duke_mtmc/index.md | 4 | ||||
| -rw-r--r-- | site/content/pages/datasets/msceleb/index.md | 28 | ||||
| -rw-r--r-- | site/content/pages/datasets/oxford_town_centre/index.md | 6 | ||||
| -rw-r--r-- | site/content/pages/datasets/uccs/index.md | 4 | ||||
| -rw-r--r-- | site/content/pages/research/02_what_computers_can_see/index.md | 7 |
8 files changed, 52 insertions, 26 deletions
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..4c9f1576 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -8,8 +8,8 @@ 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 ------------ @@ -19,10 +19,21 @@ authors: Adam Harvey ### sidebar ### end sidebar +The Microsoft Celeb dataset is a face recognition training site made entirely of images scraped from the Internet. According to Microsoft Research who created and published the dataset in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of 100,000 individuals. + +But Microsoft's ambition was bigger. They wanted to recognize 1 million individuals. As part of their dataset they released a list of 1 million target identities for researchers to identity. The identities + +https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/ + +In 2019, Microsoft CEO Brad Smith called for the governmental regulation of face recognition, an admission of his own company's inability to control their surveillance-driven business model. Yet since then, and for the last 4 years, Microsoft has willingly and actively played a significant role in accelerating growth in the very same industry they called for the government to regulate. This investigation looks look into the [MS Celeb](https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/) dataset and Microsoft Research's role in creating and distributing the largest publicly available face recognition dataset in the world to both. + + + +to spur growth and incentivize researchers, Microsoft released a dataset called [MS Celeb](https://msceleb.org), or Microsft Celeb, in which they developed and published a list of exactly 1 million targeted people whose biometrics would go on to build + + -https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology -https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm {% include 'dashboard.html' %} @@ -30,11 +41,12 @@ https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-ab ### Additional Information -- The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> +- SenseTime https://www.semanticscholar.org/paper/The-Devil-of-Face-Recognition-is-in-the-Noise-Wang-Chen/9e31e77f9543ab42474ba4e9330676e18c242e72 +- Microsoft used it https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70 +- https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology +- https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm ### 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
\ 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 |