Robust recognition of noisy and partially occluded faces using iteratively reweighted fitting of Eigenfaces

Wangmeng Zuo, Kuanquan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Robust recognition of noisy and partially occluded faces is essential for an automated face recognition system, but most appearance-based methods (e.g., Eigenfaces) are sensitive to these factors. In this paper, we propose to address this problem using an iteratively reweighted fitting of the Eigenfaces method (IRF-Eigenfaces). Unlike Eigenfaces fitting, in which a simple linear projection operation is used to extract the feature vector, the IRF-Eigenfaces method first defines a generalized objective function and then uses the iteratively reweighted least-squares (IRLS) fitting algorithm to extract the feature vector by minimizing the generalized objective function. Our simulated and experimental results on the AR database show that IRF-Eigenfaces is far superior to both Eigenfaces and to the local probabilistic method in recognizing noisy and partially occluded faces.
Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2006
Subtitle of host publication7th Pacific Rim Conference on Multimedia, Proceedings
PublisherSpringer Verlag
Pages844-851
Number of pages8
ISBN (Print)3540487662, 9783540487661
Publication statusPublished - 1 Jan 2006
EventPCM 2006: 7th Pacific Rim Conference on Multimedia - Hangzhou, China
Duration: 2 Nov 20064 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4261 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePCM 2006: 7th Pacific Rim Conference on Multimedia
CountryChina
CityHangzhou
Period2/11/064/11/06

Keywords

  • Eigenfaces
  • Face recognition
  • Iteratively reweighted least squares
  • Noise
  • Partial occlusion
  • Principal component analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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