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Learning the Face Prior for Bayesian Face Recognition

  • Chaochao Lu
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

Abstract

For the traditional Bayesian face recognition methods, a simple prior on face representation cannot cover large variations in facial poses, illuminations, expressions, aging, and occlusions in the wild. In this paper, we propose a new approach to learn the face prior for Bayesian face recognition. First, we extend Manifold Relevance Determination to learn the identity subspace for each individual automatically. Based on the structure of the learned identity subspaces, we then propose to estimate Gaussian mixture densities in the observation space with Gaussian process regression. During the training of our approach, the leave-set-out algorithm is also developed for overfitting avoidance. On extensive experimental evaluations, the learned face prior can improve the performance of the traditional Bayesian face and other related methods significantly. It is also proved that the simple Bayesian face method with the learned face prior can handle the complex intra-personal variations such as large poses and large occlusions. Experiments on the challenging LFW benchmark shows that our algorithm outperforms most of the state-of-art methods.

Keywords

Face Recognition Face Image Observation Space Fisher Vector Probabilistic Linear Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

978-3-319-10593-2_9_MOESM1_ESM.pdf (171 kb)
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chaochao Lu
    • 1
  • Xiaoou Tang
    • 1
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongChina

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