Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection

In this paper, a novel approach to facial expression recognition based on the discrete separable shearlet transform (DSST) and normalized mutual information feature selection is proposed. The approach can be divided into five steps. First, all test and training images are preprocessed. Second, DSST is applied to the preprocessed facial expression images, and all the transformation coefficients are obtained as the original feature set. Third, an improved normalized mutual information feature selection is proposed to find the optimal feature subset of the original feature set, thus we can retain the key classification information of the original data. Fourth, the feature extraction and selection of the feature space is reduced by employing linear discriminant analysis. Finally, a support vector machine is used to recognize the expressions. In this study, experimental verification was carried out on four open facial expression databases. The results show that this method can not only improve the recognition rate of facial expressions, but also significantly reduce the computational complexity and improve the system efficiency.