Fast alignment for sparse representation based face recognition

Sparse representation based face recognition (SRC) has been paid much attention in recent years. Representative algorithms are deformable sparse-representation based classification (DSRC) and shape-constrained texture matching, which focus on misalignment and shape change respectively. Although these algorithms obtain improved accuracy and robustness, their efficiency is not satisfied particularly when applied in a large-scale system. The main difficulty is the expensive calculation in aligning gallery images and the probe image. To solve these problems, this paper proposes a fast alignment strategy for sparse representation based algorithms. The key idea is to pre-compute the most expensive operation, Hessian matrix, which was needed to be calculated in each iteration. Subsequently, with help of the proposed fast alignment strategy, two algorithms, deformable SRC and shape-constrained texture matching, are extended to their fast versions, i.e., fast deformable SRC and fast shape-constrained texture matching. Experimental evaluations have been conducted on the public datasets such as Multi-PIE, FERET, and Cohn-Kanade. We demonstrate that the proposed alignment strategy greatly improves the efficiency of original algorithms without losing accuracy and robustness.