Spectral embedding based facial expression recognition with multiple features
Spectral embedding based feature fusion framework is proposed to combine the appearance based and geometry based features for facial expression recognition. A supervised multi-view spectral embedding algorithm is developed to achieve more discriminative embedding. In order to solve the out-of-sample problem, we utilize a linearization method to map unseen data to the unified low dimensional subspace discovered by the MSE algorithm. We perform a comprehensive study of five widely used facial expression features, including AAM, LBP, Multiscale WLD, SIFT descriptor and Gabor filters.
spectral embedding facial expression recognition multiple features multi-modal data fusion