Issue No. 01 - January (2012 vol. 34)
Peng Li , University College London, London
Yun Fu , University College London, London
Umar Mohammed , University College London, London
James H. Elder , University College London, London
Simon J.D. Prince , York University, Toronto
Many face recognition algorithms use “distance-based” methods: Feature vectors are extracted from each face and distances in feature space are compared to determine matches. In this paper, we argue for a fundamentally different approach. We consider each image as having been generated from several underlying causes, some of which are due to identity (latent identity variables, or LIVs) and some of which are not. In recognition, we evaluate the probability that two faces have the same underlying identity cause. We make these ideas concrete by developing a series of novel generative models which incorporate both within-individual and between-individual variation. We consider both the linear case, where signal and noise are represented by a subspace, and the nonlinear case, where an arbitrary face manifold can be described and noise is position-dependent. We also develop a “tied” version of the algorithm that allows explicit comparison of faces across quite different viewing conditions. We demonstrate that our model produces results that are comparable to or better than the state of the art for both frontal face recognition and face recognition under varying pose.
Computing methodologies, pattern recognition, applications, face and gesture recognition.
Y. Fu, J. H. Elder, U. Mohammed, S. J. Prince and P. Li, "Probabilistic Models for Inference about Identity," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 144-157, 2011.