Face recognition based on weighted wavelet transform and compressed sensing
LU Li-na, Xuelong Hu, Shuhan Chen, Lei Sun, Chunxiao Li · 2016
A face recognition algorithm based on weighted wavelet transform and compressive sensing is proposed in this paper. Considering the influence of wavelet transform on each component, the low-frequency components, the horizontal and vertical components are weighted fused. It can reduce the loss of the identification information and the dimensionality of face images. Then, principal component analysis is employed to extract facial feature matrices. Each class of the test samples is reconstructed by the sparse coefficients and the dictionary matrix. The test samples are classified in the smallest residual error by comparing with the reconstructed samples. Experimental results on ORL and FERET face databases verify that the algorithm proposed in this paper has a better recognition ability. The recognition accuracy and recognition speed of the new algorithm are better than traditional methods.