Scale invariant face recognition using probabilistic similarity measure

In video surveillance, the size of face images is very small. However, few works have been done to investigate scale invariant face recognition. Our experiments on appearance-based methods in different resolutions show that such methods as neighboring preserving embedding (NPE) preserving local structure are less effective than global ones such as linear discriminant analysis (LDA) under low-resolution. Based on the phenomena, we present a new graph embedding method FisherNPE, preserving both global and local structures on the data, and using Bayesian probabilistic similarity analysis of intensity differences between high- and low-resolution images for scale robust feature extraction. Experimental results on ORL and Yale database indicate that our method obtains good results on different resolution images.