<?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%">Ruvolo, P.</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%">Automatic cry detection in early childhood education settings</style></title><secondary-title><style face="normal" font="default" size="100%">7th IEEE International Conference on Development and Learning, 2008. ICDL 2008</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acoustic noise</style></keyword><keyword><style  face="normal" font="default" size="100%">auditory moods</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic cry detection</style></keyword><keyword><style  face="normal" font="default" size="100%">behavioural sciences computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Deafness</style></keyword><keyword><style  face="normal" font="default" size="100%">early childhood education settings</style></keyword><keyword><style  face="normal" font="default" size="100%">education</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%">human coders</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%">Mood</style></keyword><keyword><style  face="normal" font="default" size="100%">preschool classrooms</style></keyword><keyword><style  face="normal" font="default" size="100%">Prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">Working environment noise</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%">08/2008</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%">Monterey, CA</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-2661-4</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 results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">10367600</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%">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>