On the selection of 2D Krawtchouk moments for face recognition

Sparse representation of images using orthogonal two-dimensional Krawtchouk moments (2D KCMs) for face recognition is motivated by their ability to capture region-based higher-order hidden nonlinear structures from discrete coordinates of finitely supported images and the invariance of affine transformations of these moments to common geometric distortions. This paper presents the effectiveness of selecting the discriminatory set of KCMs as the global and local face features as opposed to traditional features obtained from heuristic choice of fixed-order moments or projection of the moments for recognizing an identity. The selection of significantly sparse 2D KCM-based features according to the proposed approach results in highly efficient face recognition method as compared to the other methods that use orthogonal moments such as the 2D Zernike, 2D Tchebichef or 2D Gaussian-Hermite. Experiments on challenging databases (viz., FRGC and CK-AUC) and comparisons with the well established projection, texture, and moment-based methods indicate superior recognition performance in terms of mean accuracy and robustness of the proposed holistic- or hybrid-type discriminative KCM-based method, especially when sample sizes are small and the intraclass faces have significant variations due to expressions.