Abstract

We address the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications. A set or a sequence of images provides in- formation about the variability in the appearance of the face which can be used for more robust recognition. We discuss difierent approaches to the use of this information, and show that when cast as a statis- tical hypothesis testing problem, the classiffication task leads naturally to an information-theoretic algorithm that classiffies sets of images using the relative entropy (Kullback-Leibler divergence) between the estimated density of the input set and that of stored collections of images for each class. We demonstrate the performance of the proposed algorithm on two medium-sized data sets of approximately frontal face images, and describe an application of the method as part of a view-independent recognition system.