<?xml version="1.0" encoding="UTF-8"?><xml><records><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;
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