Sparse representation-based classification using generalized weighted extended dictionary

Sparse representation-based classification (SRC) has been a breakthrough of face recognition and signal reconstruction recently. However, few face images from the same subject provide insufficient observations. Meanwhile, owing to uncertainty of training images with variations in the appearance of facial illumination, pose and facial expression, it is difficult to successfully address the issues of image recognition and signal reconstruction. Thus, how to construct an optimized comprehensive dictionary is still a non-trivial task. In this paper, we develop a generalized virtual extended dictionary of SRC (V-SRC) to deal with data uncertainty problem, establishing a weighted mechanism which assigns different coefficients between arbitrary two within-class samples to obtain the virtual samples. More specifically, we firstly construct a set of synthesized training samples by means of weighted combination of existing training samples. Secondly, we combine the original and synthesized training samples as an initial extended dictionary. Thirdly, in order to decrease the constructed error in sparse representation, we exploit an elimination scheme to gain the robust training samples with respect to the initial extended training dictionary. The final goal of the proposed method is to utilize the linear combination of competitive training samples to perform face classification. Experimental results conducted on the AR, FERET and Georgia Tech databases demonstrate the effectiveness of the proposed method especially in the case of small sample size problem.