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authoradamhrv <adam@ahprojects.com>2019-07-03 13:47:23 +0200
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+ <title>MegaPixels: What Computers Can See</title>
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+
+ <section><h1>What Computers Can See About Your Face</h1>
+<p>Rosalind Picard on Affective Computing Podcast with Lex Fridman</p>
+<ul>
+<li>we can read with an ordinary camera on your phone, from a neutral face if</li>
+<li>your heart is racing</li>
+<li>if your breating is becoming irregular and showing signs of stress</li>
+<li>how your heart rate variability power is changing even when your heart is not necessarily accelerating</li>
+<li>we can tell things about your stress even if you have a blank face</li>
+</ul>
+<p>in emotion studies</p>
+<ul>
+<li>when participants use smartphone and multiple data types are collected to understand patterns of life can predict tomorrow's mood</li>
+<li>get best results </li>
+<li>better than 80% accurate at predicting tomorrow's mood levels</li>
+</ul>
+<p>A list of 100 things computer vision can see, eg:</p>
+<ul>
+<li>age, race, gender, ancestral origin, body mass index</li>
+<li>eye color, hair color, facial hair, glasses</li>
+<li>beauty score, </li>
+<li>intelligence</li>
+<li>what you're looking at</li>
+<li>medical conditions</li>
+<li>tired, drowsiness in car</li>
+<li>affectiva: interest in product, intent to buy</li>
+</ul>
+<h2>From SenseTime paper</h2>
+<p>Exploring Disentangled Feature Representation Beyond Face Identification</p>
+<p>From <a href="https://arxiv.org/pdf/1804.03487.pdf">https://arxiv.org/pdf/1804.03487.pdf</a>
+The attribute IDs from 1 to 40 corre-spond to: ‘5 o Clock Shadow’, ‘Arched Eyebrows’, ‘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’. It’</p>
+<h2>From PubFig Dataset</h2>
+<ul>
+<li>Male</li>
+<li>Asian</li>
+<li>White</li>
+<li>Black</li>
+<li>Baby</li>
+<li>Child</li>
+<li>Youth</li>
+<li>Middle Aged</li>
+<li>Senior</li>
+<li>Black Hair</li>
+<li>Blond Hair</li>
+<li>Brown Hair</li>
+<li>Bald</li>
+<li>No Eyewear</li>
+<li>Eyeglasses</li>
+<li>Sunglasses</li>
+<li>Mustache</li>
+<li>Smiling Frowning</li>
+<li>Chubby</li>
+<li>Blurry</li>
+<li>Harsh Lighting</li>
+<li>Flash</li>
+<li>Soft Lighting</li>
+<li>Outdoor Curly Hair</li>
+<li>Wavy Hair</li>
+<li>Straight Hair</li>
+<li>Receding Hairline</li>
+<li>Bangs</li>
+<li>Sideburns</li>
+<li>Fully Visible Forehead </li>
+<li>Partially Visible Forehead </li>
+<li>Obstructed Forehead</li>
+<li>Bushy Eyebrows </li>
+<li>Arched Eyebrows</li>
+<li>Narrow Eyes</li>
+<li>Eyes Open</li>
+<li>Big Nose</li>
+<li>Pointy Nose</li>
+<li>Big Lips</li>
+<li>Mouth Closed</li>
+<li>Mouth Slightly Open</li>
+<li>Mouth Wide Open</li>
+<li>Teeth Not Visible</li>
+<li>No Beard</li>
+<li>Goatee </li>
+<li>Round Jaw</li>
+<li>Double Chin</li>
+<li>Wearing Hat</li>
+<li>Oval Face</li>
+<li>Square Face</li>
+<li>Round Face </li>
+<li>Color Photo</li>
+<li>Posed Photo</li>
+<li>Attractive Man</li>
+<li>Attractive Woman</li>
+<li>Indian</li>
+<li>Gray Hair</li>
+<li>Bags Under Eyes</li>
+<li>Heavy Makeup</li>
+<li>Rosy Cheeks</li>
+<li>Shiny Skin</li>
+<li>Pale Skin</li>
+<li>5 o' Clock Shadow</li>
+<li>Strong Nose-Mouth Lines</li>
+<li>Wearing Lipstick</li>
+<li>Flushed Face</li>
+<li>High Cheekbones</li>
+<li>Brown Eyes</li>
+<li>Wearing Earrings</li>
+<li>Wearing Necktie</li>
+<li>Wearing Necklace</li>
+</ul>
+<p>for i in {1..9};do wget <a href="http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_0$i.MP4;done;for">http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_0$i.MP4;done;for</a> i in {10..20}; do wget <a href="http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_$i.MP4;done">http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_$i.MP4;done</a></p>
+<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>
+<h2>From Leeds Sports Pose</h2>
+<p>=INDEX(A2:A9,MATCH(datasets!D1,B2:B9,0))
+=VLOOKUP(A2, datasets!A:J, 7, FALSE)</p>
+<p>Right ankle
+Right knee
+Right hip
+Left hip
+Left knee
+Left ankle
+Right wrist
+Right elbow
+Right shoulder
+Left shoulder
+Left elbow
+Left wrist
+Neck
+Head top</p>
+<p>source: <a href="http://web.archive.org/web/20170915023005/sam.johnson.io/research/lsp.html">http://web.archive.org/web/20170915023005/sam.johnson.io/research/lsp.html</a></p>
+</section>
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