<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wu, T.</style></author><author><style face="normal" font="default" size="100%">Butko, N.</style></author><author><style face="normal" font="default" size="100%">Ruvulo, P.</style></author><author><style face="normal" font="default" size="100%">Bartlett, M.</style></author><author><style face="normal" font="default" size="100%">Movellan, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Make Facial Expressions</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE 8th International Conference on Development and Learning, 2009. ICDL 2009</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Actuators</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">face detection</style></keyword><keyword><style  face="normal" font="default" size="100%">face recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">facial motor parameters</style></keyword><keyword><style  face="normal" font="default" size="100%">Feedback</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">learning (artificial intelligence)</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic heads</style></keyword><keyword><style  face="normal" font="default" size="100%">Pediatrics</style></keyword><keyword><style  face="normal" font="default" size="100%">real-time facial expression recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Robot sensing systems</style></keyword><keyword><style  face="normal" font="default" size="100%">robotic head</style></keyword><keyword><style  face="normal" font="default" size="100%">Robots</style></keyword><keyword><style  face="normal" font="default" size="100%">self-guided learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Servomechanisms</style></keyword><keyword><style  face="normal" font="default" size="100%">Servomotors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2009</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-4117-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px;&quot;&gt;This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate the computational study of how infants learn to make facial expressions.&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">10801981</style></accession-num></record></records></xml>