Using the Original and Symmetrical Face Test Samples to Perform Two-Step Collaborative Representation for Face Recognition

Abstract

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.