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Proceedings Paper

Optimal FLD algorithm for facial feature extraction
Author(s): Jian Yang; Jingyu Yang
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Paper Abstract

In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases. Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number of training samples). Then, the transformed space is divided into two subspaces: the null space of within- class scatter matrix and its orthogonal complement, from which two cases of optimal discriminant vectors are selected respectively. Finally, we test our method on ORL face database, and achieve a recognition rate of 97% with a minimum distance classifier or a nearest neighbor classifier. The experimental results indicate that our approach is better than classical Eigenfaces and Fisherfaces with respect to recognition performance.

Paper Details

Date Published: 5 October 2001
PDF: 7 pages
Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); doi: 10.1117/12.444212
Show Author Affiliations
Jian Yang, Nanjing Univ. of Science and Technology (China)
Jingyu Yang, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 4572:
Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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