<?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>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%">Ruvolo, P.</style></author><author><style face="normal" font="default" size="100%">Fasel, I.</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%">Auditory mood detection for social and educational robots</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Robotics and Automation, 2008. ICRA 2008</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">auditory mood detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">educational robot</style></keyword><keyword><style  face="normal" font="default" size="100%">Educational robots</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">emotional speech database</style></keyword><keyword><style  face="normal" font="default" size="100%">face detection</style></keyword><keyword><style  face="normal" font="default" size="100%">hearing</style></keyword><keyword><style  face="normal" font="default" size="100%">interactive robotic application</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%">Mood Prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">object recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Robotics and Automation Robots</style></keyword><keyword><style  face="normal" font="default" size="100%">social mood</style></keyword><keyword><style  face="normal" font="default" size="100%">social robot</style></keyword><keyword><style  face="normal" font="default" size="100%">Speech</style></keyword><keyword><style  face="normal" font="default" size="100%">USA Councils</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2008</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%">Pasadena, CA</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-1646-2</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;Social robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al., 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al., 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">10014826</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fasel, I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Real-Time Object Detectors: Probabilistic Generative Approaches</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Cognitive Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%"> University of California, San Diego</style></publisher><pub-location><style face="normal" font="default" size="100%">San Diego</style></pub-location><volume><style face="normal" font="default" size="100%">Doctoral dissertation</style></volume><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>9</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fasel, I.</style></author><author><style face="normal" font="default" size="100%">Fortenberry, B.</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%">MPT: the Machine Perception Toolbox</style></title><alt-title><style face="normal" font="default" size="100%">Computer Vision and Image Understanding</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><orig-pub><style face="normal" font="default" size="100%">A generative framework for boosting with applications to real-time eye coding</style></orig-pub></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>