@inproceedings{5e4ccb896b084a4a9a20d0e468731d56,
title = "Robust recognition of noisy and partially occluded faces using iteratively reweighted fitting of Eigenfaces",
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.",
keywords = "Eigenfaces, Face recognition, Iteratively reweighted least squares, Noise, Partial occlusion, Principal component analysis",
author = "Wangmeng Zuo and Kuanquan Wang and Dapeng Zhang",
year = "2006",
month = jan,
day = "1",
language = "English",
isbn = "3540487662",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "844--851",
booktitle = "Advances in Multimedia Information Processing - PCM 2006",
address = "Germany",
note = "PCM 2006: 7th Pacific Rim Conference on Multimedia ; Conference date: 02-11-2006 Through 04-11-2006",
}