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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kotsia, I. Nikolaidis, N. Pitas, I. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Inf., Aristotelian Univ. of Thessaloniki, Greece (Kotsia, I.; Nikolaidis, N.; Pitas, I.) |
| Abstract | In this paper, a novel class of support vector machines (SVM) is introduced to deal with facial expression recognition. The proposed classifier incorporates statistic information about the classes under examination into the classical SVM. The developed system performs facial expression recognition in facial videos. The grid tracking and deformation algorithm used tracks the Candide grid over time as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of Candide nodes is used as an input to the bank of novel SVM classifiers, that are utilized to recognize the six basic facial expressions. The experiments on the Cohn-Kanade database show a recognition accuracy of 98.2%. |
| File Size | 174107 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.366303 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Videos Support vector machines Support vector machine classification Statistics Data mining Informatics Spatial databases Humans Muscles Candide Grid Facial Expression Recognition Facial Action Coding System Support Vector Machines |
| Content Type | Text |
| Resource Type | Article |
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