Robust Sparse Coding for Face Recognition

As a powerful tool for statistical signal modeling, sparse representation (or sparse coding) has been successfully used in image processing applications, and recently has led to promising results in face recognition and texture classification. Based on the findings that natural images can be generally coded by structural primitives (e.g., edges and line segments) that are qualitatively similar in form to simple cell receptive fields, sparse coding techniques represent a natural image using a small number of atoms parsimoniously chosen out of an over-complete dictionary.