Abstract: 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 an underlying cause (a latent identity variable, or LIV). In recognition we evaluate the probability that two faces have the same underlying cause. Since image generation is noisy, we can never be exactly certain what this cause was, so we integrate (marginalize) over all possible causes. We present examples of identification and verification and show that the LIV approach outperforms equivalent distance-based algorithms. Moreover, other advantages include: (i) a natural approach to changes in pose and lighting (ii) the ability to implement novel algorithms that have no distance-based equivalent (iii) a principled way to combine multiple observations and prior information.
About the speaker: Dr. Simon Prince is a lecturer in the Department of Computer Science at University College London. He was an undergraduate at UCL where he studied Psychology. His doctoral work was at the University of Oxford, in the Department of Experimental Psychology. He subsequently worked in the Laboratory of Physiology in Oxford for two years as Post Doc with Andrew Parker. In 2000 he returned to UCL where he undertook the Masters by Research in Computer Vision, Image Processing, Graphics and Simulation. Upon completion of this degree he moved for two and a half years to Singapore where he was a post-doctoral research fellow in the Department of Electrical and Computer Engineering in the National University of Singapore. Following this, he moved to Toronto, Canada, where he worked as a post-doc for James Elder in the Centre for Vision Research in York University until 2005.