Robust face recognition for occluded real‐world images using constrained probabilistic sparse network

Abstract

Abstract Aiming at the occluded real‐world face images across illumination, pose, expression, and resolution variations, a robust face recognition for occluded real‐world images using constrained probabilistic sparse network is presented. A constrained probabilistic sparse representation network is constructed to obtain the features of all the training images from a global perspective, and the new network nodes are generated through the random combination of the training images. In the probabilistic sparse representation network, the probabilities of each class of the sparse subspace that the occluded test images individually belong to are defined and calculated. The final classifications of the test images are determined by the joint maximum probability of the network nodes. Meanwhile the second‐order gradient constraint is the first introduced in the probabilistic sparse representation network. It is found that the constraint uses the adjacent pixels of the face images to obtain the local texture similarity, and further use the local texture similarity to distinguish the occlusion and non‐occlusion parts. Thus the constraint can reduce the influence of the occlusion part on face recognition. Extensive experiments with the 12 existing methods on the five face databases demonstrate that the recognition rate of the proposed method is the best than the non‐deep learning methods compared, and the proposed method can obtain nearly the same recognition rate with an advantage of a very less time consumption compared to the state‐of‐the‐art deep learning methods.