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| author | adamhrv <adam@ahprojects.com> | 2019-06-27 23:58:12 +0200 |
|---|---|---|
| committer | adamhrv <adam@ahprojects.com> | 2019-06-27 23:58:12 +0200 |
| commit | ae165ef1235a6997d5791ca241fd3fd134202c92 (patch) | |
| tree | c258e6837c579d4d4baa42d85dca78b036ca022b /site/content | |
| parent | 5e6803d488b2ea7379d608932214b201b80d9eac (diff) | |
editing tyupos
Diffstat (limited to 'site/content')
36 files changed, 395 insertions, 143 deletions
diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index d766e258..b5c6bf1a 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -18,7 +18,7 @@ authors: Adam Harvey ### sidebar ### end sidebar -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 60 FPS, 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"[^duke_mtmc_orig]. +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 60 FPS, 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".[^duke_mtmc_orig] For this analysis of the Duke MTMC dataset over 100 publicly available research papers that used the dataset were analyzed to find out who's using the dataset and where it's being used. The results show that the Duke MTMC 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 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. diff --git a/site/content/pages/datasets/helen/assets/background.jpg b/site/content/pages/datasets/helen/assets/background.jpg Binary files differnew file mode 100755 index 00000000..6958a2b2 --- /dev/null +++ b/site/content/pages/datasets/helen/assets/background.jpg diff --git a/site/content/pages/datasets/helen/assets/ijb_c_montage.jpg b/site/content/pages/datasets/helen/assets/ijb_c_montage.jpg Binary files differnew file mode 100755 index 00000000..3b5a0e40 --- /dev/null +++ b/site/content/pages/datasets/helen/assets/ijb_c_montage.jpg diff --git a/site/content/pages/datasets/helen/assets/index.jpg b/site/content/pages/datasets/helen/assets/index.jpg Binary files differnew file mode 100755 index 00000000..7268d6ad --- /dev/null +++ b/site/content/pages/datasets/helen/assets/index.jpg diff --git a/site/content/pages/datasets/helen/index.md b/site/content/pages/datasets/helen/index.md new file mode 100644 index 00000000..d44c9b98 --- /dev/null +++ b/site/content/pages/datasets/helen/index.md @@ -0,0 +1,30 @@ +------------ + +status: draft +title: HELEN +desc: HELEN Face Dataset +subdesc: HELEN (under development) +slug: helen +cssclass: dataset +image: assets/background.jpg +year: 2000 +published: 2019-4-18 +updated: 2019-4-18 +authors: Adam Harvey + +------------ + +## HELEN + +### sidebar +### end sidebar + +[ page under development ] + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} + +{% include 'cite_our_work.html' %} + +### Footnotes diff --git a/site/content/pages/datasets/ibm_dif/assets/background.jpg b/site/content/pages/datasets/ibm_dif/assets/background.jpg Binary files differnew file mode 100755 index 00000000..6958a2b2 --- /dev/null +++ b/site/content/pages/datasets/ibm_dif/assets/background.jpg diff --git a/site/content/pages/datasets/ibm_dif/assets/ijb_c_montage.jpg b/site/content/pages/datasets/ibm_dif/assets/ijb_c_montage.jpg Binary files differnew file mode 100755 index 00000000..3b5a0e40 --- /dev/null +++ b/site/content/pages/datasets/ibm_dif/assets/ijb_c_montage.jpg diff --git a/site/content/pages/datasets/ibm_dif/assets/index.jpg b/site/content/pages/datasets/ibm_dif/assets/index.jpg Binary files differnew file mode 100755 index 00000000..7268d6ad --- /dev/null +++ b/site/content/pages/datasets/ibm_dif/assets/index.jpg diff --git a/site/content/pages/datasets/ibm_dif/index.md b/site/content/pages/datasets/ibm_dif/index.md new file mode 100644 index 00000000..4c620e95 --- /dev/null +++ b/site/content/pages/datasets/ibm_dif/index.md @@ -0,0 +1,30 @@ +------------ + +status: draft +title: MegaFace +desc: MegaFace Dataset +subdesc: MegaFace contains 670K identities and 4.7M images +slug: megaface +cssclass: dataset +image: assets/background.jpg +year: 2016 +published: 2019-4-18 +updated: 2019-4-18 +authors: Adam Harvey + +------------ + +## MegaFace + +### sidebar +### end sidebar + +[ page under development ] + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} + +{% include 'cite_our_work.html' %} + +### Footnotes diff --git a/site/content/pages/datasets/ijb_c/index.md b/site/content/pages/datasets/ijb_c/index.md index d1ac769b..70c71f19 100644 --- a/site/content/pages/datasets/ijb_c/index.md +++ b/site/content/pages/datasets/ijb_c/index.md @@ -21,36 +21,19 @@ authors: Adam Harvey [ page under development ] -The IARPA Janus Benchmark C (IJB–C) is a dataset of web images used for face recognition research and development. The IJB–C dataset contains 3,531 people +The IARPA Janus Benchmark C (IJB–C) is a dataset of web images used for face recognition research and development. The IJB–C dataset contains 3,531 people from 21,294 images and 3,531 videos. The list of 3,531 names are activists, artists, journalists, foreign politicians, and public speakers. -Among the target list of 3,531 names are activists, artists, journalists, foreign politicians, +Key Findings: - - -- Subjects 3531 -- Templates: 140739 -- Genuine Matches: 7819362 -- Impostor Matches: 39584639 - - -Why not include US Soliders instead of activists? - - -was creted by Nobilis, a United States Government contractor is used to develop software for the US intelligence agencies as part of the IARPA Janus program. - -The IARPA Janus program is - -these representations must address the challenges of Aging, Pose, Illumination, and Expression (A-PIE) by exploiting all available imagery. - - -- metadata annotations were created using crowd annotations -- created by Nobilis -- used mechanical turk +- metadata annotations were created using crowd annotations on Mechanical Turk +- The dataset was creatd Nobilis - made for intelligence analysts - improve performance of face recognition tools - by fusing the rich spatial, temporal, and contextual information available from the multiple views captured by today’s "media in the wild" +The dataset includes Creative Commons images + The name list includes @@ -92,7 +75,7 @@ The first 777 are non-alphabetical. From 777-3531 is alphabetical From original papers: https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf -Collection for the dataset began by identifying CreativeCommons subject videos, which are often more scarce thanCreative Commons subject images. Search terms that re-sulted in large quantities of person-centric videos (e.g. “in-terview”) were generated and translated into numerous lan-guages including Arabic, Korean, Swahili, and Hindi to in-crease diversity of the subject pool. Certain YouTube userswho upload well-labeled, person-centric videos, such as the World Economic Forum and the International University Sports Federation were also identified. Titles of videos per-taining to these search terms and usernames were scrapedusing the YouTube Data API and translated into English us-ing the Yandex Translate API4. Pattern matching was per-formed to extract potential names of subjects from the trans-lated titles, and these names were searched using the Wiki-data API to verify the subject’s existence and status as a public figure, and to check for Wikimedia Commons im-agery. Age, gender, and geographic region were collectedusing the Wikipedia API.Using the candidate subject names, Creative Commonsimages were scraped from Google and Wikimedia Com-mons, and Creative Commons videos were scraped fromYouTube. After images and videos of the candidate subjectwere identified, AMT Workers were tasked with validat-ing the subject’s presence throughout the video. The AMTWorkers marked segments of the video in which the subjectwas present, and key frames +Collection for the dataset began by identifying CreativeCommons subject videos, which are often more scarce than Creative Commons subject images. Search terms that re-sulted in large quantities of person-centric videos (e.g. “in-terview”) were generated and translated into numerous lan-guages including Arabic, Korean, Swahili, and Hindi to in-crease diversity of the subject pool. Certain YouTube userswho upload well-labeled, person-centric videos, such as the World Economic Forum and the International University Sports Federation were also identified. Titles of videos per-taining to these search terms and usernames were scrapedusing the YouTube Data API and translated into English us-ing the Yandex Translate API4. Pattern matching was per-formed to extract potential names of subjects from the trans-lated titles, and these names were searched using the Wiki-data API to verify the subject’s existence and status as a public figure, and to check for Wikimedia Commons im-agery. Age, gender, and geographic region were collectedusing the Wikipedia API.Using the candidate subject names, Creative Commonsimages were scraped from Google and Wikimedia Com-mons, and Creative Commons videos were scraped fromYouTube. After images and videos of the candidate subjectwere identified, AMT Workers were tasked with validat-ing the subject’s presence throughout the video. The AMTWorkers marked segments of the video in which the subjectwas present, and key frames IARPA funds Italian researcher https://www.micc.unifi.it/projects/glaivejanus/ diff --git a/site/content/pages/datasets/megaface/assets/background.jpg b/site/content/pages/datasets/megaface/assets/background.jpg Binary files differnew file mode 100755 index 00000000..6958a2b2 --- /dev/null +++ b/site/content/pages/datasets/megaface/assets/background.jpg diff --git a/site/content/pages/datasets/megaface/assets/ijb_c_montage.jpg b/site/content/pages/datasets/megaface/assets/ijb_c_montage.jpg Binary files differnew file mode 100755 index 00000000..3b5a0e40 --- /dev/null +++ b/site/content/pages/datasets/megaface/assets/ijb_c_montage.jpg diff --git a/site/content/pages/datasets/megaface/assets/index.jpg b/site/content/pages/datasets/megaface/assets/index.jpg Binary files differnew file mode 100755 index 00000000..7268d6ad --- /dev/null +++ b/site/content/pages/datasets/megaface/assets/index.jpg diff --git a/site/content/pages/datasets/megaface/index.md b/site/content/pages/datasets/megaface/index.md new file mode 100644 index 00000000..4c620e95 --- /dev/null +++ b/site/content/pages/datasets/megaface/index.md @@ -0,0 +1,30 @@ +------------ + +status: draft +title: MegaFace +desc: MegaFace Dataset +subdesc: MegaFace contains 670K identities and 4.7M images +slug: megaface +cssclass: dataset +image: assets/background.jpg +year: 2016 +published: 2019-4-18 +updated: 2019-4-18 +authors: Adam Harvey + +------------ + +## MegaFace + +### sidebar +### end sidebar + +[ page under development ] + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} + +{% include 'cite_our_work.html' %} + +### Footnotes diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index 5095da3d..453c1522 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -87,7 +87,8 @@ Until now, that data has been freely harvested from the Internet and packaged in  -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 used the MS Celeb face dataset to build their algorithms and advertise the results. Interestingly, Microsoft's [corporate version](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/) of the paper does not mention they used the MS Celeb datset, but the [open-access version](https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70) published on arxiv.org does. It states that Microsoft Research analyzed their algorithms using "the MS-Celeb-1M low-shot learning benchmark task."[^one_shot] +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 used the MS Celeb face dataset to build their algorithms and advertise the results. Interestingly, Microsoft's [corporate version](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/) of the paper does not mention they used the MS Celeb datset, but the [open-access version](https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70) published on arxiv.org does. It states that Microsoft analyzed their algorithms "on the MS-Celeb-1M low-shot learning [benchmark task](https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/)"[^one_shot], which is described as a refined version of the original MS-Celeb-1M face dataset. + Typically researchers will phrase this differently and say that they only use a dataset to validate their algorithm. But validation data can't be easily separated from the training process. To develop a neural network model, image training datasets are split into three parts: train, test, and validation. Training data is used to fit a model, and the validation and test data are used to provide feedback about the hyperparameters, biases, and outputs. In reality, test and validation data steers and influences the final results of neural networks. diff --git a/site/content/pages/datasets/oxford_town_centre/index.md b/site/content/pages/datasets/oxford_town_centre/index.md index bd340113..c2e3e7a7 100644 --- a/site/content/pages/datasets/oxford_town_centre/index.md +++ b/site/content/pages/datasets/oxford_town_centre/index.md @@ -29,11 +29,11 @@ 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 increasingly 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. The upper camera, a public CCTV camera installed for security, is most likely the camera used to create this dataset. -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. +The camera can be seen pointing in the same direction as the dataset's view in this [public image](https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/), and the researchers used other existing public CCTV cameras for additional [research projects](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) increasing the likelihood that they could have had access to this camera. -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 it was probably a weekday after rubbish removal. +The capture date is estimated to be during late November or early December in 2007 or 2008. The text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was likely around this time. 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. And 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, it was probably recorded in 2007. The slight waste residue near the bench and the lack street vendors that typically appear on a weekend, suggest that it was perhaps a weekday after rubbish removal.  diff --git a/site/content/pages/datasets/who_goes_there/assets/background.jpg b/site/content/pages/datasets/who_goes_there/assets/background.jpg Binary files differnew file mode 100755 index 00000000..6958a2b2 --- /dev/null +++ b/site/content/pages/datasets/who_goes_there/assets/background.jpg diff --git a/site/content/pages/datasets/who_goes_there/assets/ijb_c_montage.jpg b/site/content/pages/datasets/who_goes_there/assets/ijb_c_montage.jpg Binary files differnew file mode 100755 index 00000000..3b5a0e40 --- /dev/null +++ b/site/content/pages/datasets/who_goes_there/assets/ijb_c_montage.jpg diff --git a/site/content/pages/datasets/who_goes_there/assets/index.jpg b/site/content/pages/datasets/who_goes_there/assets/index.jpg Binary files differnew file mode 100755 index 00000000..7268d6ad --- /dev/null +++ b/site/content/pages/datasets/who_goes_there/assets/index.jpg diff --git a/site/content/pages/datasets/who_goes_there/index.md b/site/content/pages/datasets/who_goes_there/index.md new file mode 100644 index 00000000..feb9896d --- /dev/null +++ b/site/content/pages/datasets/who_goes_there/index.md @@ -0,0 +1,30 @@ +------------ + +status: draft +title: Who Goes There Dataset +desc: Who Goes There Dataset +subdesc: Who Goes There (page under development) +slug: who_goes_there +cssclass: dataset +image: assets/background.jpg +year: 2016 +published: 2019-4-18 +updated: 2019-4-18 +authors: Adam Harvey + +------------ + +## Who Goes There + +### sidebar +### end sidebar + +[ page under development ] + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} + +{% include 'cite_our_work.html' %} + +### Footnotes diff --git a/site/content/pages/research/00_introduction/index.md b/site/content/pages/research/00_introduction/index.md deleted file mode 100644 index ad8e2200..00000000 --- a/site/content/pages/research/00_introduction/index.md +++ /dev/null @@ -1,113 +0,0 @@ ------------- - -status: draft -title: 00: Introduction -desc: Introduction to Megapixels -slug: 00_introduction -published: 2018-12-15 -updated: 2018-12-15 -authors: Megapixels - ------------- - -# MegaPixels - -+ Posted: Dec. 15 -+ Author: Adam Harvey - - -Facial recognition is a scam. - -It's extractive and damaging industry that's built on the biometric backbone of the Internet. - -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. - -There is no such thing as *face recognition*. 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. - -Committed to developing robust solutions with superhuman accuracy, the industry has repeatedly undermined itself by never actually developing anything close to "face recognition". - -There is only biased feature vector clustering and probabilistic thresholding. - -## If you don't have data, you don't have a product. - -Yesterday's [decision](https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV) 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 - -## Cost of Faces - -Univ Houston paid subjects $20/ea -http://web.archive.org/web/20170925053724/http://cbl.uh.edu/index.php/pages/research/collecting_facial_images_from_multiples_in_texas - -FaceMeta facedataset.com - -- BASIC: 15,000 images for $6,000 USD -- RECOMMENDED: 50,000 images for $12,000 USD -- ADVANCED: 100,000 images for $18,000 USD* - - -## Use Your Own Biometrics First - -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. - -### Motivation - -Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to developing and validating face recognition technologies. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">videos on YouTube</a>. - -During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is powering the global facial recognition industry. - -While many of these datasets include public figures such as politicians, athletes, and actors; they also include many non-public figures: digital activists, students, pedestrians, and semi-private shared photo albums are all considered "in the wild" and fair game for research projects. Some images are used with creative commons licenses, yet others were taken in unconstrained scenarios without awareness or consent. At first glance it appears many of the datasets were created for seemingly harmless academic research, but when examined further it becomes clear that they're also used by foreign defense agencies. - -The MegaPixels site is based on an earlier [installation](https://ahprojects.com/megapixels-glassroom) (also supported by Mozilla) at the [Tactical Tech Glassroom](https://theglassroom.org/) in London in 2017; and a commission from the Elevate arts festival curated by Berit Gilma about pedestrian recognition datasets in 2018, and research during [CV Dazzle](https://cvdazzle.com) from 2010-2015. Through the many prototypes, conversations, pitches, PDFs, and false starts this project has endured during the last 5 years, it eventually evolved into something much different than originally imagined. Now, as datasets become increasingly influential in shaping the computational future, it's clear that they must be critically analyzed to understand the biases, shortcomings, funding sources, and contributions to the surveillance industry. However, it's misguided to only criticize these datasets for their flaws without also praising their contribution to society. Without publicly available facial analysis datasets there would be less public discourse, less open-source software, and less peer-reviewed research. Public datasets can indeed become a vital public good for the information economy but as this projects aims to illustrate, many ethical questions arise about consent, intellectual property, surveillance, and privacy. - -<!-- who provided funding to research, development this project understand the role these datasets have played in creating biometric surveillance technologies. --> - - - - -Ever since the first computational facial recognition research project by the CIA in the early 1960s, data has always played a vital role in the development of our biometric future. Without facial recognition datasets there would be no facial recognition. Datasets are an indispensable part of any artificial intelligence system because, as Geoffrey Hinton points out: -> Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - [Geoffrey Hinton](https://www.youtube.com/watch?v=-eyhCTvrEtE) - -Algorithms learn from datasets. And we program algorithms by building datasets. But datasets aren't like code. There's no programming language made of data except for the data itself. - ------ - -Ignore content below these lines - ------ - - -It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating. - -Now the future has arrived and it doesn't make sense. Facial recognition works yet it doesn't actually work. Facial recognition is cheap and accessible but also expensive and out of control. Facial recognition research has achieved headline grabbing superhuman accuracies over 99.9% yet facial recognition is also dangerously inaccurate. During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using facial recognition software that mistakenly identified an alarming 98% of people as criminals [^met_police], which perhaps is a crime itself. - - -MegaPixels is an online art project that explores the history of facial recognition from the perspective of datasets. To paraphrase the artist Trevor Paglen, whoever controls the dataset controls the meaning. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth. MegaPixels does not start with a conclusion, a moralistic slant, or a - -Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. [^asci_option_red]. - - -I hope that this project will illuminate the darker areas of strange world of facial recognition that have not yet received attention and encourage discourse in academic, industry, and . By no means do I believe discourse can save the day. Nor do I think creating artwork can. In fact, I'm not exactly sure what the outcome of this project will be. The project is not so much what I publish here but what happens after. This entire project is only a prologue. - -As McLuhan wrote, "You can't have a static, fixed position in the electric age". And in our hyper-connected age of mass surveillance, artificial intelligece, and unevenly distributed virtual futures the most irrational thing to be is rational. Increasingly the world is becoming a contradiction where people use surveillance to protest surveillance, use - - - -Like many projects, MegaPixels had spent years meandering between formats, unfeasible budgets, and was generally too niche of a subject. The basic idea for this project, as proposed to the original [Glass Room](https://tacticaltech.org/projects/the-glass-room-nyc/) installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the [LFW](/datasets/lfw) facial recognition dataset. The idea was based on my reaction to all the datasets I'd come across during research for the CV Dazzle project. I'd noticed strange datasets created for training and testing face detection algorithms. Most were created in labratory settings and their interpretation of face data was very strict. - - - -### for other post - -It was the early 2000s. Face recognition was new and no one seemed sure how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure the borders. It was the future that John Ashcroft demanded with the Total Information Awareness act of the 2003. It was a future that academics helped build. It was a future that celebrities helped build. And it was a future that - -A decade earlier the Department of Homeland Security and the Counterdrug Technology Development Program Office initated a feasibilty study called FERET (FacE REcognition Technology) to "develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties [^feret_website]." - -One problem with FERET dataset was that the photos were in controlled settings. For face recognition to work it would have to be used in uncontrolled settings. Even newer datasets such as the Multi-PIE (Pose, Illumination, and Expression) from Carnegie Mellon University included only indoor photos of cooperative subjects. Not only were the photos completely unrealistic, CMU's Multi-Pie included only 18 individuals and cost $500 for academic use [^cmu_multipie_cost], took years to create, and required consent from every participant. - - - -## Add progressive gan of FERET - -[^multi_domain]: Freitas, Tiago de Pereira; Anjos, Andre ́; Marcel, Sébastien. "Heterogeneous Face Recognition Using Domain Specific Units". 2015. <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8565895> -[^met_police]: Sharman, Jon. "Metropolitan Police's facial recognition technology 98% inaccurate, figures show". 2018. <https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html> -[^asci_option_red]: Calle, Dan. "Supercomptuers". 1997. <http://ei.cs.vt.edu/~history/SUPERCOM.Calle.HTML> -[^nist_feret]: "Face Recognition Technology (FERET)". <https://www.nist.gov/programs-projects/face-recognition-technology-feret>
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Introduction to Megapixels +slug: 00_introduction +cssclass: dataset +published: 2018-12-15 +updated: 2018-12-15 +authors: Adam Harvey + +------------ + +# Introduction + +Face recognition has become the focal point for ... + +Add 68pt landmarks animation + +But biometric currency is ... + +Add rotation 3D head + +Inflationary... + +Add Theresea May 3D + +(comission for CPDP) + +Add info from the AI Traps talk + + ++ Posted: Dec. 15 ++ Author: Adam Harvey + + + +``` +load_file /site/research/00_introduction/assets/summary_countries_top.csv +country, Xcitations +``` + +Paragraph text to test css formatting. Paragraph text to test css formatting. Paragraph text to test css formatting. Paragraph text to test css formatting. Paragraph text to test css formatting. + + + +[ page under development ] + +
\ No newline at end of file diff --git a/site/content/pages/research/02_what_computers_can_see/index.md b/site/content/pages/research/_what_computers_can_see/index.md index faa4ab17..5b7458c6 100644 --- a/site/content/pages/research/02_what_computers_can_see/index.md +++ b/site/content/pages/research/_what_computers_can_see/index.md @@ -13,6 +13,18 @@ sync: false # What Computers Can See About Your Face +Rosalind Picard on Affective Computing Podcast with Lex Fridman +- we can read with an ordinary camera on your phone, from a neutral face if +- your heart is racing +- if your breating is becoming irregular and showing signs of stress +- how your heart rate variability power is changing even when your heart is not necessarily accelerating +- we can tell things about your stress even if you have a blank face + +in emotion studies +- when participants use smartphone and multiple data types are collected to understand patterns of life can predict tomorrow's mood +- get best results +- better than 80% accurate at predicting tomorrow's mood levels + A list of 100 things computer vision can see, eg: @@ -30,7 +42,7 @@ A list of 100 things computer vision can see, eg: 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’ +The attribute IDs from 1 to 40 corre-spond to: ‘5 o Clock Shadow’, ‘Arched Eyebrows’, ‘Attractive’, ‘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’. 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There,US Embassy Jerusalem,46886434@N04,https://flickr.com/photos/usembassyta,US,IL +805,Who Goes There,US Embassy New Zealand,46907600@N02,https://flickr.com/photos/us_embassy_newzealand,US,NZ +94,Who Goes There,US Embassy Bolivia,47642741@N05,https://flickr.com/photos/usembassybolivia,US,BO +30,Who Goes There,US Embassy Italy,48297107@N03,https://flickr.com/photos/ambasciatausa,US,IT +3,Who Goes There,US Embassy New Delhi,54323860@N06,https://flickr.com/photos/usembassynewdelhi,US,IN +15,Who Goes There,US Consulate General Munich,78557833@N04,https://flickr.com/photos/usconsulatemunich,US,DE +1,VGG Face,British Embassy Beijing,45458403@N04,https://flickr.com/photos/ukinchina,UK,CN diff --git a/site/content/pages/research/munich_security_conference/assets/montage_placeholder.jpg b/site/content/pages/research/munich_security_conference/assets/montage_placeholder.jpg Binary files differnew file mode 100644 index 00000000..b64348f4 --- /dev/null +++ b/site/content/pages/research/munich_security_conference/assets/montage_placeholder.jpg diff --git a/site/content/pages/research/munich_security_conference/assets/pie_placeholder.png b/site/content/pages/research/munich_security_conference/assets/pie_placeholder.png Binary files differnew file mode 100644 index 00000000..e3f3648c --- /dev/null +++ b/site/content/pages/research/munich_security_conference/assets/pie_placeholder.png diff --git a/site/content/pages/research/munich_security_conference/assets/summary_countries.png b/site/content/pages/research/munich_security_conference/assets/summary_countries.png Binary files differnew file mode 100755 index 00000000..1389e35c --- /dev/null +++ b/site/content/pages/research/munich_security_conference/assets/summary_countries.png diff --git a/site/content/pages/research/munich_security_conference/assets/summary_sources.png b/site/content/pages/research/munich_security_conference/assets/summary_sources.png Binary files differnew file mode 100755 index 00000000..09c96d51 --- /dev/null +++ b/site/content/pages/research/munich_security_conference/assets/summary_sources.png diff --git a/site/content/pages/research/munich_security_conference/index.md b/site/content/pages/research/munich_security_conference/index.md new file mode 100644 index 00000000..92b24603 --- /dev/null +++ b/site/content/pages/research/munich_security_conference/index.md @@ -0,0 +1,48 @@ +------------ + +status: published +title: MSC +slug: munich-security-conference +desc: Analyzing the Transnational Flow of Facial Recognition Data +subdesc: Where does face data originate and who's using it? +cssclass: dataset +image: assets/background.jpg +published: 2019-4-18 +updated: 2019-4-19 +authors: Adam Harvey + +------------ + +# 5,000 Embassy Flickr Photos Were used to Train Face Recognition + +[page under devlopment] + +Intro paragraph. + +[ add montage of extracted faces here] + + + + + +=== columns 2 + + + +=========== + + + +=== end columns + +{% include 'supplementary_header.html' %} + +[ add a download button for CSV data ] + +``` +load_file /site/research/munich_security_conference/assets/embassy_counts_public.csv +Images, Dataset, Embassy, Flickr ID, URL, Guest, Host +``` + + +{% include 'cite_our_work.html' %} |
