Mixed Error Coding for Face Recognition with Mixed Occlusions

Abstract

Mixed occlusions commonly consist in real-world face images and bring with it great challenges for automatic face recognition. The existing methods usually utilize the same reconstruction error to code the occluded test image with respect to the labeled training set and simultaneously to estimate the occlusion/feature support. However, this error coding model might not be applicable for face recognition with mixed occlusions. For mixed occlusions, the error used to code the test image, called the discriminative error, and the error used to estimate the occlusion support, called the structural error, might have totally different behaviors. By combining the two various errors with the occlusion support, we present an extended error coding model, dubbed Mixed Error Coding (MEC). To further enhance discriminability and feature selection ability, we also incorporate into MEC the hidden feature selection technology of the subspace learning methods in the domain of the image gradient orientations. Experiments demonstrate the effectiveness and robustness of the proposed MEC model in dealing with mixed occlusions.