University of Cambridge > Talks.cam > Microsoft Research Machine Learning and Perception Seminars > identity variables for face recognition: from distance based methods to probabilistic inference

identity variables for face recognition: from distance based methods to probabilistic inference

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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. Finally we demonstrate how this framework can be applied to the more general problem of clustering face images.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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