Robust face recognition with partially occluded images based on a single or a small number of training samples

This paper investigates the problem of face recognition with partially occluded images without assuming prior information about the distortion, and with only a single training image or a small number of training images for each class to be identified. A new approach is presented, which is an extension of our previous posterior union model. The new approach is formulated by using a similarity measure in place of the probability measure, thereby allowing the use of a single training image to represent a class. The new approach achieves improved robustness to partial occlusion by focusing the recognition mainly on the matched local regions, which are selected automatically subject to an optimality criterion to maximize the similarity of the correct class. Two databases, XM2VTS and AR, have been used to evaluate the new approach. The results indicate that the new system is able to perform as well as an oracle model for dealing with various simulated and realistic partial distortions/occlusions without requiring prior information.