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     </a>
     <div class='links'>
       <a href="/datasets/">Datasets</a>
-      <a href="/research/">Research</a>
       <a href="/about/">About</a>
     </div>
   </header>
@@ -42,7 +41,17 @@
     </div>
   </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>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:</p>
+  <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><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>
+<p>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.</p>
+<p>The MegaPixels site is based on an earlier <a href="https://ahprojects.com/megapixels-glassroom">installation</a> (also supported by Mozilla) at the <a href="https://theglassroom.org/">Tactical Tech Glassroom</a> 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 <a href="https://cvdazzle.com">CV Dazzle</a> 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.</p>
+<!-- who provided funding to research, development this project understand the role these datasets have played in creating biometric surveillance technologies. -->
+
+
+
+
+<p>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:</p>
 <blockquote><p>Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - <a href="https://www.youtube.com/watch?v=-eyhCTvrEtE">Geoffrey Hinton</a></p>
 </blockquote>
 <p>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.</p>
diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html
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     </a>
     <div class='links'>
       <a href="/datasets/">Datasets</a>
-      <a href="/research/">Research</a>
       <a href="/about/">About</a>
     </div>
   </header>
@@ -79,6 +78,7 @@
 </ul>
 <ul>
 <li>"Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf </li>
+<li>IBM DiF: "Faces with region size less than 50x50 or inter-ocular distance of less than 30 pixels were discarded. Faces with non-frontal pose, or anything beyond being slightly tilted to the left or the right, were also discarded."</li>
 </ul>
 <div class="footnotes">
 <hr>
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 6826955f..9389bf84 100644
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     </a>
     <div class='links'>
       <a href="/datasets/">Datasets</a>
-      <a href="/research/">Research</a>
       <a href="/about/">About</a>
     </div>
   </header>
@@ -127,6 +126,149 @@
 <li>Wearing Necktie</li>
 <li>Wearing Necklace</li>
 </ul>
+<h2>From Market 1501</h2>
+<p>The 27 attributes are:</p>
+<table>
+<thead><tr>
+<th style="text-align:center">attribute</th>
+<th style="text-align:center">representation in file</th>
+<th style="text-align:center">label</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">gender</td>
+<td style="text-align:center">gender</td>
+<td style="text-align:center">male(1), female(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">hair length</td>
+<td style="text-align:center">hair</td>
+<td style="text-align:center">short hair(1), long hair(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">sleeve length</td>
+<td style="text-align:center">up</td>
+<td style="text-align:center">long sleeve(1), short sleeve(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">length of lower-body clothing</td>
+<td style="text-align:center">down</td>
+<td style="text-align:center">long lower body clothing(1), short(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">type of lower-body clothing</td>
+<td style="text-align:center">clothes</td>
+<td style="text-align:center">dress(1), pants(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">wearing hat</td>
+<td style="text-align:center">hat</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying backpack</td>
+<td style="text-align:center">backpack</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying bag</td>
+<td style="text-align:center">bag</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying handbag</td>
+<td style="text-align:center">handbag</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">age</td>
+<td style="text-align:center">age</td>
+<td style="text-align:center">young(1), teenager(2), adult(3), old(4)</td>
+</tr>
+<tr>
+<td style="text-align:center">8 color of upper-body clothing</td>
+<td style="text-align:center">upblack, upwhite, upred, uppurple, upyellow, upgray, upblue, upgreen</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">9 color of lower-body clothing</td>
+<td style="text-align:center">downblack, downwhite, downpink, downpurple, downyellow, downgray, downblue, downgreen,downbrown</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+</tbody>
+</table>
+<p>source: <a href="https://github.com/vana77/Market-1501_Attribute/blob/master/README.md">https://github.com/vana77/Market-1501_Attribute/blob/master/README.md</a></p>
+<h2>From DukeMTMC</h2>
+<p>The 23 attributes are:</p>
+<table>
+<thead><tr>
+<th style="text-align:center">attribute</th>
+<th style="text-align:center">representation in file</th>
+<th style="text-align:center">label</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">gender</td>
+<td style="text-align:center">gender</td>
+<td style="text-align:center">male(1), female(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">length of upper-body clothing</td>
+<td style="text-align:center">top</td>
+<td style="text-align:center">short upper body clothing(1), long(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">wearing boots</td>
+<td style="text-align:center">boots</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">wearing hat</td>
+<td style="text-align:center">hat</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying backpack</td>
+<td style="text-align:center">backpack</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying bag</td>
+<td style="text-align:center">bag</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">carrying handbag</td>
+<td style="text-align:center">handbag</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">color of shoes</td>
+<td style="text-align:center">shoes</td>
+<td style="text-align:center">dark(1), light(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">8 color of upper-body clothing</td>
+<td style="text-align:center">upblack, upwhite, upred, uppurple, upgray, upblue, upgreen, upbrown</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+<tr>
+<td style="text-align:center">7 color of lower-body clothing</td>
+<td style="text-align:center">downblack, downwhite, downred, downgray, downblue, downgreen, downbrown</td>
+<td style="text-align:center">no(1), yes(2)</td>
+</tr>
+</tbody>
+</table>
+<p>source: <a href="https://github.com/vana77/DukeMTMC-attribute/blob/master/README.md">https://github.com/vana77/DukeMTMC-attribute/blob/master/README.md</a></p>
+<h2>From H3D Dataset</h2>
+<p>The joints and other keypoints (eyes, ears, nose, shoulders, elbows, wrists, hips, knees and ankles)
+The 3D pose inferred from the keypoints.
+Visibility boolean for each keypoint
+Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder)
+Body type (male, female or child)</p>
+<p>source: <a href="https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/">https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/</a></p>
 </section>
 
   </div>
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     </a>
     <div class='links'>
       <a href="/datasets/">Datasets</a>
-      <a href="/research/">Research</a>
       <a href="/about/">About</a>
     </div>
   </header>
   <div class="content content-">
     
   <section><h1>Research Blog</h1>
-</section><div class='research_index'><a href='/research/00_introduction/'><section class='wide'><img src='data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==' alt='Research post' /><section><h1>00: Introduction</h1><h2></h2></section></section></a><a href='/research/01_from_1_to_100_pixels/'><section class='wide'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/site/research/01_from_1_to_100_pixels/assets/intro.jpg' alt='Research post' /><section><h1>From 1 to 100 Pixels</h1><h2>A breif description of this post, appears in the index page overview</h2></section></section></a></div>
+</section>
 
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