An efficient method for occluded face recognition

During the last two decades, a series of subspace methods have succeeded in achieving a satisfactory performance for face recognition tasks, but have always failed when partial occlusions occur. This paper combines the subspace techniques with probabilistic models, and aims at achieving invariance to occlusions. The concept underlying the proposed method is that two faces with the same identity, even though one of them is partially occluded, tend to be similar in the uncorrupted areas. The similarity value measured from the error distributions can then be exploited for identification. Experiments show the robustness of this novel method against various kinds of occlusion.