earticle

영어

In allusion to such problems in the traditional face recognition methods as poor recognition accuracy and dissatisfactory processing effect for directivity and anisotropic characteristic in face data, lasso regularized Gabor shearlet face multivariate sparse function approximation algorithm is proposed in this article. Firstly, Gabor improved shearlet algorithm is adopted at the level of the face-image biological signals for the sparse expansion representation of the face data characteristics, and meanwhile this algorithm is also adopted to extract the geometrical characteristics of the expansion face with directivity and anisotropic characteristic. Secondly, in order to balance the algorithm effect, lasso regularization theory is introduced therein to control and weigh the relation between the fidelity and the smoothness of the face data. Finally, the corresponding simulation experiment is carried out to compare the proposed algorithm and the existing algorithms in the standard test database in order to verify the advantages of the proposed algorithm in the aspect of face recognition accuracy and efficiency.