<?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%">Littlewort, G.</style></author><author><style face="normal" font="default" size="100%">Whitehill, J.</style></author><author><style face="normal" font="default" size="100%">Wu, T.</style></author><author><style face="normal" font="default" size="100%">Fasel, I.</style></author><author><style face="normal" font="default" size="100%">Frank, M.</style></author><author><style face="normal" font="default" size="100%">Movellan, J.</style></author><author><style face="normal" font="default" size="100%">Bartlett, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The computer expression recognition toolbox (CERT)</style></title><secondary-title><style face="normal" font="default" size="100%">2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3D orientation</style></keyword><keyword><style  face="normal" font="default" size="100%">Accuracy</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic real-time facial expression recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">CERT</style></keyword><keyword><style  face="normal" font="default" size="100%">computer expression recognition toolbox</style></keyword><keyword><style  face="normal" font="default" size="100%">Detectors</style></keyword><keyword><style  face="normal" font="default" size="100%">dual core laptop</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Encoding</style></keyword><keyword><style  face="normal" font="default" size="100%">extended Cohn-Kanade</style></keyword><keyword><style  face="normal" font="default" size="100%">Face</style></keyword><keyword><style  face="normal" font="default" size="100%">face recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">facial action unit coding system</style></keyword><keyword><style  face="normal" font="default" size="100%">facial expression dataset</style></keyword><keyword><style  face="normal" font="default" size="100%">Facial features</style></keyword><keyword><style  face="normal" font="default" size="100%">FACS</style></keyword><keyword><style  face="normal" font="default" size="100%">Gold</style></keyword><keyword><style  face="normal" font="default" size="100%">Image coding</style></keyword><keyword><style  face="normal" font="default" size="100%">software tool</style></keyword><keyword><style  face="normal" font="default" size="100%">software tools</style></keyword><keyword><style  face="normal" font="default" size="100%">two-alternative forced choice task</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2011</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%">Santa Barbara, CA</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-9140-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;p1&quot;&gt;We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different prototypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+[1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80%. In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second.&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">12007742</style></accession-num></record><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><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Whitehill, J.</style></author><author><style face="normal" font="default" size="100%">Littlewort, G.</style></author><author><style face="normal" font="default" size="100%">Fasel, I.</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%">Toward Practical Smile Detection</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic facial expression recognition research</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Pattern Recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Biometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Face</style></keyword><keyword><style  face="normal" font="default" size="100%">Face and gesture recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">face recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">feature representation</style></keyword><keyword><style  face="normal" font="default" size="100%">human-level expression recognition accuracy</style></keyword><keyword><style  face="normal" font="default" size="100%">illumination conditions</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Enhancement</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">image registration image representation</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 approaches</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning Models</style></keyword><keyword><style  face="normal" font="default" size="100%">n Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">practical smile detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Smiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Subtraction Technique</style></keyword><keyword><style  face="normal" font="default" size="100%">training data set</style></keyword><keyword><style  face="normal" font="default" size="100%">visual databases</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%">11/2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">2106-2111</style></pages><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;Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.&lt;/span&gt;&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><section><style face="normal" font="default" size="100%">2106</style></section></record><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%">Littlewort, G.</style></author><author><style face="normal" font="default" size="100%">Bartlett, M.</style></author><author><style face="normal" font="default" size="100%">Fasel, I.</style></author><author><style face="normal" font="default" size="100%">Chenu, J.</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%">Analysis of machine learning methods for real-time recognition of facial expressions from video</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Pattern Recognition: Face Processing Workshop</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Littlewort, G.</style></author><author><style face="normal" font="default" size="100%">Bartlett, M.</style></author><author><style face="normal" font="default" size="100%">Chenu, J.</style></author><author><style face="normal" font="default" size="100%">Fasel, I.</style></author><author><style face="normal" font="default" size="100%">Kanda, T.</style></author><author><style face="normal" font="default" size="100%">Ishiguro, H.</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%">Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">1563-1570</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><reprint-edition><style face="normal" font="default" size="100%">MIT Press</style></reprint-edition><section><style face="normal" font="default" size="100%">1563</style></section></record></records></xml>