Enlarge the training set based on inter-class relationship for face recognition from one image per person

PLoS One. 2013 Jul 16;8(7):e68539. doi: 10.1371/journal.pone.0068539. Print 2013.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Discriminant Analysis
  • Face*
  • Humans
  • Pattern Recognition, Automated / methods*
  • Principal Component Analysis

Grants and funding

This work is supported by the Guangdong Natural Science Foundation, grant number S2012040007988 (http://gdsf.gdstc.gov.cn/). This work is also partially supported by the Shenzhen Academy of Metrology & Quality Inspection, grant number 2012-YA07 (http://www.smq.com.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.