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Probabilistic models for inference about iden‐ tity (2012)
| Venue: | IEEE TPAMI |
| Citations: | 52 - 0 self |
BibTeX
@ARTICLE{Li12probabilisticmodels,
author = {Peng Li and Yun Fu and Umar Mohammed and James Elder and Simon J. D. Prince},
title = {Probabilistic models for inference about iden‐ tity},
journal = {IEEE TPAMI},
year = {2012},
pages = {144--157}
}
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Abstract
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 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 non-linear 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 or better than the state of the art for both frontal face recognition and face recognition under varying pose.
Keyphrases
iden tity probabilistic model latent identity variable feature space face recognition different approach explicit comparison feature vector underlying identity cause non-linear case between-individual variation arbitrary face manifold novel generative model several underlying cause frontal face recognition distance-based method linear case recognition algorithm abstract many idea concrete tied version
