Improving CCA via spectral components selection for facial expression recognition

摘要

In this paper, we propose a novel canonical correlation analysis (CCA) algorithm for facial expression recognition. In contrast to the traditional CCA algorithm, the proposed method is capable of selecting the optimal spectral components of the training data matrix in modelling the linear correlation between the facial feature vectors and the corresponding expression class membership vectors. We formulate this spectral selection problem as a sparse optimization problem, where the ℓ1-norm penalty is adopted to this goal. To recognize the emotion category of each facial image, we present a linear regression formula to predict the emotion class membership for each facial image. The experiments on the JAFFE facial expression database confirm the better recognition performance of the proposed method.