Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades. Accordingly, feature selection has become more and more important and plays a critical role for face data description and recognition. In this paper, the authors propose a trainable feature selection algorithm based on the regularized frame for face recognition. By enforcing a sparsity penalty term on the Minimum Squared Error (MSE) criterion, they cast the feature selection problem into a combinatorial sparse approximation problem, which can be solved by greedy methods or convex relaxation methods.