Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6087
Title: Face recognition with multi-resolution spectral feature images
Authors: Sun, ZL
Lam, KMK 
Dong, ZY
Wang, H
Gao, QW
Zheng, CH
Keywords: Face recognition technology
Image analysis
Pattern recognition
Issue Date: 13-Feb-2013
Publisher: Public Library of Science
Source: PLoS one, Feb 2013, v. 8, no. 2, e55700, p. 1-12 How to cite?
Journal: PLoS one 
Abstract: The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.
URI: http://hdl.handle.net/10397/6087
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0055700
Rights: © 2013 Sun et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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