Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7011
Title: Enlarge the training set based on inter-class relationship for face recognition from one image per person
Authors: Li, Q
Wang, HJ
You, J 
Li, ZM
Li, JX
Issue Date: 16-Jul-2013
Publisher: Public Library of Science
Source: PLoS one, 16 July 2013, v. 8, no. 7, e68539, p.1-9 How to cite?
Journal: PLoS one 
Abstract: In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two) images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA), Fisher linear discriminant analysis (LDA), and locality preserving projections (LPP) and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.
URI: http://hdl.handle.net/10397/7011
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0068539
Rights: © 2013 Li 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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