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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 67dcbb5e..907b73d6 100644 --- a/site/public/research/02_what_computers_can_see/index.html +++ b/site/public/research/02_what_computers_can_see/index.html @@ -70,7 +70,21 @@ </div> </section> - <section><p>A list of 100 things computer vision can see, eg:</p> + <section><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> @@ -84,7 +98,7 @@ <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’, ‘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’</p> +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> |