Facial expression recognition in videos using a novel multi-class support vector machines variant
Kotsia, Irene and Nikolaidis, Nikolaos and Pitas, Ioannis (2007) Facial expression recognition in videos using a novel multi-class support vector machines variant. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 15 - 20 April 2007, Honolulu, Hawai, USA.
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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%.
|Item Type:||Conference or Workshop Item (Paper)|
|Research Areas:||A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Intelligent Environments Research Group
|Depositing User:||Devika Mohan|
|Date Deposited:||06 Dec 2012 06:47|
|Last Modified:||18 Mar 2015 16:03|
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