Recognizing facial expressions based on Gabor filter selection

Recognition of human emotional state is an important component for efficient human-computer interaction. In this paper a method of Gabor filter selection for facial expression recognition is investigated. We first preprocess facial images based on affine transform to normalize the faces. Then the using of a separability judgment is proposed to evaluate the separability of different Gabor filters, and only use those filters that can better separate different expressions. In the recognition process a PCA and FLDA multiclassifier scheme is used. The experiment result shows that the introducing of Gabor filter selection can not only reduce the dimension of feature space but also reduce the computation complexity significantly, while retaining high recognition rate of above 93%.