A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation

Proposed is a simple yet fast and robust approach to face recognition. This approach is developed specifically to address the challenges due to variations of illumination, expression and occlusion, when studying the facial images of a large population. The proposed approach exploits an improved collaborative representation algorithm. First, we construct an initial dictionary by extracting the multi-scale and multi-orientation Gabor features of the image. Second, we design a new discriminative dictionary by an improved K-SVD algorithm, so that the query sample can be better represented for classification. Finally, l2-norm of coding residual is calculated by collaborative representation based classification with regularized least square method (CRC-RLS) and the class of test sample is obtained. Experiments on two benchmark face datasets show that the proposed method can achieve high classification accuracy and is comparatively low in terms of time-consumption, compared to the CRC-RLS algorithm.