Patch-Based Similarity HMMs for Face Recognition with a Single Reference Image

Abstract

In this paper we present a new architecture for face recognition with a single reference image, which completely separates the training process from the recognition process. In the training stage, by using a database containing various individuals, the spatial relations between face components are represented by two Hidden Markov Models (HMMs), one modeling within-subject similarities, and the other modeling inter-subject differences. This allows us during the recognition stage to take a pair of face images, neither of which has been seen before, and to determine whether or not they come from the same individual. Whilst other face-recognition HMMs use Maximum Likelihood criterion, we test our approach using both Maximum Likelihood and Maximum a Posteriori (MAP) criterion, and find that MAP provides better results. Importantly, the training database can be entirely separated from the gallery and test images: this means that adding new individuals to the system can be done without re-training. We present results based upon models trained on the FERET training dataset, and demonstrate that these give satisfactory recognition rates on both the FERET database itself and more impressively the unseen AR database. When compared to other HMM based face recognition techniques, our algorithm is of much lower complexity due to the small size of our observation sequence.