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diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index fa485ac0..f1d59366 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -4,20 +4,47 @@ <title>MegaPixels</title> <meta charset="utf-8" /> <meta name="author" content="Adam Harvey" /> - <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="description" content="Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition" /> + <meta property="og:title" content="MegaPixels: Microsoft Celeb Dataset"/> + <meta property="og:type" content="website"/> + <meta property="og:image" content="https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/background.jpg" /> + <meta property="og:url" content="https://megapixels.cc/datasets/msceleb/"/> + <meta property="og:site_name" content="MegaPixels" /> <meta name="referrer" content="no-referrer" /> - 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<div class='splash'>Microsoft Celeb</div> + <div class='page_name'>Microsoft Celeb</div> </a> <div class='links'> <a href="/datasets/">Datasets</a> @@ -26,20 +53,20 @@ </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'>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 + <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</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> <div>2016</div> </div><div class='meta'> <div class='gray'>Images</div> - <div>1,000,000 </div> + <div>10,000,000 </div> </div><div class='meta'> <div class='gray'>Identities</div> <div>100,000 </div> </div><div class='meta'> <div class='gray'>Purpose</div> - <div>Large-scale face recognition</div> + <div>Face recognition</div> </div><div class='meta'> <div class='gray'>Created by</div> <div>Microsoft Research</div> @@ -49,210 +76,133 @@ </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>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> + </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="https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/">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 an initial training dataset of 100,000 individuals' images to accelerate research into recognizing a larger target list of one million people "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 1">1</a></p> +<p>These one million people, defined by Microsoft 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. This fact should not allow Microsoft nor anyone else to use their biometrics for research and development of surveillance technology. Many names in the 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; artists critical of surveillance including Trevor Paglen, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glenn Greenwald; Data and Society founder danah boyd; and even Julie Brill, the former FTC commissioner responsible for protecting consumer 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> +<p>Below is a selection of 24 names from the full target list, 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://www.msceleb.org">msceleb.org</a>. You can email <a href="mailto:msceleb@microsoft.com?subject=MS-Celeb-1M Removal Request&body=Dear%20Microsoft%2C%0A%0AI%20recently%20discovered%20that%20you%20use%20my%20identity%20for%20commercial%20use%20in%20your%20MS-Celeb-1M%20dataset%20used%20for%20research%20and%20development%20of%20face%20recognition.%20I%20do%20not%20wish%20to%20be%20included%20in%20your%20dataset%20in%20any%20format.%20%0A%0APlease%20remove%20my%20name%20and%2For%20any%20associated%20images%20immediately%20and%20send%20a%20confirmation%20once%20you've%20updated%20your%20%22Top1M_MidList.Name.tsv%22%20file.%0A%0AThanks%20for%20promptly%20handing%20this%2C%0A%5B%20your%20name%20%5D">msceleb@microsoft.com</a> to have your name removed. Names appearing with * indicate that Microsoft also distributed your images.</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>Adrian Chen</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> +<td>Ai Weiwei*</td> +<td>Artist</td> </tr> <tr> -<td>Julie Brill</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Aram Bartholl</td> +<td>Internet artist</td> </tr> <tr> -<td>Jonathan Zittrain</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Astra Taylor</td> +<td>Author, director, activist</td> </tr> <tr> -<td>Bruce Schneier</td> -<td>m.095js</td> -<td>Cryptologist and author</td> -<td>yes</td> +<td>Alexander Madrigal</td> +<td>Journalist</td> </tr> <tr> -<td>Julie Brill</td> -<td>m.0bs3s9g</td> -<td>x</td> -<td>x</td> +<td>Bruce Schneier*</td> +<td>Cryptologist</td> </tr> <tr> -<td>Kim Zetter</td> -<td>/m/09r4j3</td> -<td>x</td> -<td>x</td> +<td>danah boyd</td> +<td>Data & Society founder</td> </tr> <tr> -<td>Ethan Zuckerman</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Edward Felten</td> +<td>Former FTC Chief Technologist</td> </tr> <tr> -<td>Jill Magid</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Evgeny Morozov*</td> +<td>Tech writer, researcher</td> </tr> <tr> -<td>Kyle McDonald</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Glenn Greenwald*</td> +<td>Journalist, author</td> </tr> <tr> -<td>Trevor Paglen</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Hito Steyerl</td> +<td>Artist, writer</td> </tr> <tr> -<td>R. Luke DuBois</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>James Risen</td> +<td>Journalist</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> +<td>Jeremy Scahill*</td> +<td>Journalist</td> </tr> <tr> -<td>Edward Felten</td> -<td>/m/028_7k</td> -<td>x</td> -<td>x</td> +<td>Jill Magid</td> +<td>Artist</td> </tr> <tr> -<td>Evgeny Morozov</td> -<td>/m/05sxhgd</td> -<td>Scholar and technology critic</td> -<td>yes</td> +<td>Jillian York</td> +<td>Digital rights activist</td> </tr> <tr> -<td>danah boyd</td> -<td>/m/06zmx5</td> -<td>Data and Society founder</td> -<td>x</td> +<td>Jonathan Zittrain</td> +<td>EFF board member</td> </tr> <tr> -<td>Bruce Schneier</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Julie Brill</td> +<td>Former FTC Commissioner</td> </tr> <tr> -<td>Laura Poitras</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Kim Zetter</td> +<td>Journalist, author</td> </tr> <tr> -<td>Trevor Paglen</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Laura Poitras*</td> +<td>Filmmaker</td> </tr> <tr> -<td>Astra Taylor</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Luke DuBois</td> +<td>Artist</td> </tr> <tr> -<td>Shoshanaa Zuboff</td> -<td>x</td> -<td>x</td> -<td>x</td> +<td>Michael Anti</td> +<td>Political blogger</td> </tr> <tr> -<td>Eyal Weizman</td> -<td>m.0g54526</td> -<td>x</td> -<td>x</td> +<td>Manal al-Sharif*</td> +<td>Womens's rights activist</td> </tr> <tr> -<td>Aram Bartholl</td> -<td>m.06_wjyc</td> -<td>x</td> -<td>x</td> +<td>Shoshana Zuboff</td> +<td>Author, academic</td> </tr> <tr> -<td>James Risen</td> -<td>m.09pk6b</td> -<td>x</td> -<td>x</td> +<td>Trevor Paglen</td> +<td>Artist, researcher</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> +</div></div></section><section><p>After publishing this list, researchers affiliated with Microsoft Asia then worked with researchers affiliated 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 April 10, 2019 <a href="https://www.ft.com/content/9378e7ee-5ae6-11e9-9dde-7aedca0a081a">article</a> published by Financial Times based on data surfaced during this investigation, Samm Sacks (a senior fellow at the New America think tank) commented that this research raised "red flags because of the nature of the technology, the author's affiliations, combined with what we know about how this technology is being deployed in China right now". Adding, that "the [Chinese] government is using these technologies to build surveillance systems and to detain minorities [in Xinjiang]".<a class="footnote_shim" name="[^madhu_ft]_1"> </a><a href="#[^madhu_ft]" class="footnote" title="Footnote 2">2</a></p> +<p>Four more papers published by SenseTime, which also use the MS Celeb dataset, raise similar flags. SenseTime is a computer vision surveillance company that until <a href="https://uhrp.org/news-commentary/china%E2%80%99s-sensetime-sells-out-xinjiang-security-joint-venture">April 2019</a> provided surveillance to Chinese authorities to monitor and track Uighur Muslims in Xinjiang province, and had been <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">flagged</a> numerous times as having potential links to human rights violations.</p> +<p>One of the 4 SenseTime papers, "<a href="https://www.semanticscholar.org/paper/Exploring-Disentangled-Feature-Representation-Face-Liu-Wei/1fd5d08394a3278ef0a89639e9bfec7cb482e0bf">Exploring Disentangled Feature Representation Beyond Face Identification</a>", shows how SenseTime was 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.</p> +<p>Earlier in 2019, Microsoft President and Chief Legal Officer <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 a stand against such potential misuse, and had decided to not sell face recognition to an unnamed United States agency, citing a lack of accuracy. The software was not suitable 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 had upgraded their ethics, but that Microsoft publicly acknowledged it can't sell a data-driven product without data. In other words, 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 inaccurate facial recognition services like Microsoft's Azure Cognitive Service also would not be able to see at all.</p> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/msceleb_montage.jpg' alt=' A visualization of 2,000 of the 100,000 identity included in the image dataset distributed by Microsoft Research. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> A visualization of 2,000 of the 100,000 identity included in the image dataset distributed by Microsoft Research. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section><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 analyze their algorithms and advertise the results. Interestingly, Microsoft's <a href="https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/">corporate version</a> of the paper 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-access version</a> published on arxiv.org explicitly mentions that Microsoft Research introspected their algorithms "on the MS-Celeb-1M low-shot learning benchmark task."</p> +<p>We suggest that if Microsoft Research wants to make biometric data publicly available for surveillance research and development, they should start with releasing their researchers' own biometric data, instead of scraping the Internet for journalists, artists, writers, actors, athletes, musicians, and academics.</p> </section><section> <h3>Who used Microsoft Celeb?</h3> @@ -300,7 +250,7 @@ <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> @@ -313,25 +263,23 @@ <h2>Supplementary Information</h2> -</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" +</section><section><h3>References</h3><section><ul class="footnotes"><li>1 <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>2 <a name="[^madhu_ft]" class="footnote_shim"></a><span class="backlinks"><a href="#[^madhu_ft]_1">a</a></span>Murgia, Madhumita. Microsoft worked with Chinese military university on artificial intelligence. Financial Times. April 10, 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> |