Facial Expression Recognition Based on Adaptive Weights Sparse Canonical Correlation Analysis

To solve the problem that when the dimension of variables was high,canonical correlation analysis couldn't give a stable model of the problem,a facial expression recognition method based on adaptive weights sparse canonical correlation analysis was proposed.Sparse canonical correlation analysis attached a constraint of coefficient convergence,some of the factors in the basis vectors converged to zero,therefore it would be able to remove some useless variables for the facial expression recognition.In the process of solving sparse canonical correlation analysis,sparse weight was a fixed value,therefore the method of adaptive weights was used to reduce the error when solving the sparse canonical vector.Resultson Jaffe and Cohn-Kanade tests of facial expression database show that the proposed method is correctness and effectiveness.