Abstract:

A face recognition method named WT-CSRC was proposed by using wavelet transform (WT) and a collaboration of double-dictionary's sparse representation-based classification (CSRC). Firstly, the proposed method used principal component analysis (PCA) to achieve the fusion of three high-frequency detail sub-images which were generated by WT, and a integrated high-frequency detail image could be obtained; then, features extracted from the low-frequency images and high-frequency detail images by PCA were used to construct the low-frequency feature space and high-frequency detail space; and low-frequency dictionary and high-frequency dictionary could be constructed by samples' projection on two kinds of feature space. Finally, face images could be classified by a collaborative classification via sparse representation in two dictionaries, and the reliability of the recognition could be enhanced by using the cross correlation coefficient. Experimental results show that, the proposed method has high recognition rate with strong illumination and expression robustness with acceptable time efficiency.

Key words: face recognition, double-dictionary, collaborative sparse representation, cross correlation coefficient