A unified framework of subspace and distance metric learning for face recognition

Qingshan Liu, Dimitris N. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper, we propose a unified scheme of subspace and distance metric learning under the Bayesian framework for face recognition. According to the local distribution of data, we divide the k-nearest neighbors of each sample into the intra-person set and the inter-person set, and we aim to learn a distance metric in the embedding subspace, which can make the distances between the sample and its intra-person set smaller than the distances between it and its interperson set. To reach this goal, we define two variables, that is, the intra-person distance and the inter-person distance, which are from two different probabilistic distributions, and we model the goal with minimizing the overlap between two distributions. Inspired by the Bayesian classification error estimation, we formulate it by minimizing the Bhattachyrra coefficient between two distributions. The power of the proposed approach are demonstrated by a series of experiments on the CMU-PIE face database and the extended YALE face database.

Original languageEnglish (US)
Title of host publicationAnalysis and Modeling of Faces and Gestures - Third International Workshop, AMFG 2007, Proceedings
PublisherSpringer Verlag
Pages250-260
Number of pages11
ISBN (Print)9783540756897
DOIs
StatePublished - 2007
Event3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007 - Rio de Janeiro, Brazil
Duration: Oct 20 2007Oct 20 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007
Country/TerritoryBrazil
CityRio de Janeiro
Period10/20/0710/20/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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