Facial expression analysis by Kernel Eigenspace Method based on Class features (KEMC) using non-linear basis for separation of expression-classes (TA-P2)

Author(s) : Yohei Kosaka (School of Information Science, Japan Advanced Institute of Science and Technology, Japan) Kazunori Kotani (School of Information Science, Japan Advanced Institute of Science and Technology, Japan)

Abstract : In the facial expression recognition by analyzing feature-vectors with linear transformation, the accuracy of recognition is depending on expression-classes. The accuracy falls sharply when the feature vector of the expression-class has a distribution with difficult linear separation in the feature-space. This paper describes a new method of facial expression analysis and recognition by using non-linear transformation for separating each expression-classes. Our new method, namely KEMC, consists of the non-linear transformation defined by kernel functions for transforming higher dimensional space and EMC ( Eigenspace Method based on Class features). This paper also shows experimental results of facial expression classification by KEMC.

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