Appearance factorization based facial expression recognition and synthesis

Abstract

Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. We address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. We conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.