Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31924
Title: Face recognition based on 2D Fisherface approach
Authors: Jing, XY
Wong, HS
Zhang, D 
Keywords: 2D discriminant vector
2D principal component
Discriminative feature extraction
Two-dimensional (2D) Fisherface approach
Issue Date: 2006
Publisher: Elsevier
Source: Pattern recognition, 2006, v. 39, no. 4, p. 707-710 How to cite?
Journal: Pattern recognition 
Abstract: Two-dimensional (2D) discrimination analysis using methods such as 2D PCA and Image LDA is of interest in face recognition because it extracts discriminative features faster than one-dimensional (1D) discrimination analysis. However, existing 2D methods generally use more discriminative features and take longer to test than 1D methods. 2D PCA in particular cannot make full use of the Fisher discriminant criterion. Image LDA also has drawbacks in that it cannot perform 2D principal component analysis and discards components with poor discriminative capabilities. In addition, existing 2D methods cannot provide an automatic strategy to choose 2D principal components or discriminant vectors. In this paper, we propose 2D Fisherface, a novel discrimination approach that combines the two-stage "PCA+LDA" strategy and 2D discrimination techniques. It can extract face discriminative features by automatically selecting two-dimensional principal components and discriminant vectors. Using the AR database as the test data, it is shown that the proposed approach is faster and more effective than several representative 1D and 2D discrimination methods.
URI: http://hdl.handle.net/10397/31924
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2005.10.020
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