FACIAL EXPRESSION ANALYSIS BY KERNEL EIGENSPACE METHOD BASED ON CLASS FEATURES (KEMC) USING NON-LINEAR BASIS FOR SEPARATION OF EXPRESSION-CLASSES

ARSTIUCT In the facial expression recognition by analyzing featurevcctom with linear traisfbrmation. ill1 accuracy 01- rccognition is depending on expression-classes. The accuracy falls remarkably when feature vectors oS expression-classes are linearly tion-separable in a feature space. This paper describes a new method 01- 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 kemcl 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.