<?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%">Butko, N.</style></author><author><style face="normal" font="default" size="100%">Theocharous, G.</style></author><author><style face="normal" font="default" size="100%">Philipose, 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%">Automated facial affect analysis for one-on-one tutoring applications</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%">automated facial affect analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">automated tutoring system</style></keyword><keyword><style  face="normal" font="default" size="100%">behavioural sciences computing</style></keyword><keyword><style  face="normal" font="default" size="100%">computer vision technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Context</style></keyword><keyword><style  face="normal" font="default" size="100%">decision making</style></keyword><keyword><style  face="normal" font="default" size="100%">education</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">face recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Human</style></keyword><keyword><style  face="normal" font="default" size="100%">human computer interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">Labeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Mood</style></keyword><keyword><style  face="normal" font="default" size="100%">n Histograms</style></keyword><keyword><style  face="normal" font="default" size="100%">one-on-one tutoring application</style></keyword><keyword><style  face="normal" font="default" size="100%">s Intelligent tutoring systems</style></keyword><keyword><style  face="normal" font="default" size="100%">student mood analysis</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&gt;&lt;span style=&quot;color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px; background-color: rgba(0, 0, 0, 0.0470588);&quot;&gt;In this paper, we explore the use of computer vision techniques to analyze students' moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student's mood and affective state. We find that the problem of accurately determining a child's mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">12007758</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%">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%">Whitehill, J.</style></author><author><style face="normal" font="default" size="100%">Movellan, Javier R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A discriminative approach to frame-by-frame head pose tracking</style></title><secondary-title><style face="normal" font="default" size="100%">8th IEEE International Conference on Automatic Face Gesture Recognition, 2008. FG '08</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">accuracy drift</style></keyword><keyword><style  face="normal" font="default" size="100%">continuous video sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">controlled illumination condition</style></keyword><keyword><style  face="normal" font="default" size="100%">discriminative approach</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 appearance</style></keyword><keyword><style  face="normal" font="default" size="100%">frame-by-frame head pose tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Laboratories</style></keyword><keyword><style  face="normal" font="default" size="100%">Lighting</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic heads</style></keyword><keyword><style  face="normal" font="default" size="100%">mean square error methods</style></keyword><keyword><style  face="normal" font="default" size="100%">pose estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">root-mean-square error tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">System testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Video sequences</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%">09/2008</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%">Amsterdam</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-2153-4</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; background-color: rgba(0, 0, 0, 0.0470588);&quot;&gt;We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (&amp;lt; 20) subjects under controlled illumination conditions on continuous video sequences. In contrast, the system presented in this paper was both trained and tested on a much larger database, GENKI, spanning tens of thousands of different subjects, illuminations, and geographical locations from images on the Web. Our pose estimator achieves accuracy of 5.82deg, 5.65deg, and 2.96deg root-mean-square (RMS) error for yaw, pitch, and roll, respectively. A set of 4000 images from this dataset, labeled for pose, was collected and released for use by the research community.&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">10571793</style></accession-num></record></records></xml>