Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30505
Title: Robust recognition of noisy and partially occluded faces using iteratively reweighted fitting of eigenfaces
Authors: Zuo, W
Wang, K
Zhang, D 
Keywords: Eigenfaces
Face recognition
Iteratively reweighted least squares
Noise
Partial occlusion
Principal component analysis
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 4261 LNCS, p. 844-851 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
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.
Description: PCM 2006: 7th Pacific Rim Conference on Multimedia, Hangzhou, 2-4 November 2006
URI: http://hdl.handle.net/10397/30505
ISBN: 3540487662
9783540487661
ISSN: 0302-9743 (print)
1611-3349 (online)
Appears in Collections:Conference Paper

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