Face Recognition Based on Circularly Symmetrical Gabor Transforms and Collaborative Representation

Abstract

Compared to the traditional Gabor transform, the circularly symmetrical Gabor transform (CSGT) not only retains the characteristics of local and multi-resolution analysis, but also has the remarkable advantages of less redundancy and rotational invariance. Simultaneously, the collaborative representation-based classification with regularized least square (CRC-RLS) overcomes the shortcoming of the high computational complexity in the sparse representation-based classification (SRC). However, both classification algorithms still use the global features of the image, ignoring the importance of local features in the face images. In this paper, the face images are first mapped onto the CSGT domain, and then the amplitude images are chosen as the sample images. Finally, CRC is used to classify different faces. The experimental results on AR, FERET and Extended Yale B face databases show that the proposed algorithm achieves higher recognition rates and better robustness.