Discriminant spectral analysis for facial expression recognition

Spectral analysis is a recently proposed method for feature extraction. Studies show that the features extracted by spectral analysis can also be used to classification. In this paper, we propose a nonlinear feature extraction method called discriminant spectral analysis (DSA) algorithm for facial expression recognition. DSA takes both intra-locality and inter-locality structure of the data into account, and the features extracted by DSA have more discriminant power than traditional methods. Moreover, DSA is a nonlinear method which can effectively discover the intrinsic nonlinear manifold structure hidden in the data. Experimental results on Cohn-Kanade and JAFFE facial databases show the effectiveness of DSA algorithm.