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Proceedings Paper

Facial feature extraction based on GSLDA
Author(s): Li Meng; Yong Cai; Yuanxing Li; Min Wang
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Paper Abstract

In this paper, a general and efficient facial feature extraction approach, global search linear discriminant analysis (GSLDA), is presented. It is designed to solve the puzzle of standard linear discriminant analysis (LDA) for small sample size problems (SSSP). Compared with PCA-LDA, in GSLDA, raw data dimension can be greatly decreased without discarding important discriminant information. In this process, all basis vectors of the non-null eigen-space of the scatter matrix is worked out, and then the well-known global search strategy, genetic algorithm, is enrolled to select basis vectors to construct a new feature space which has optimal discriminant ability. In contrast with PCA, this approach reserves more information for recognition. Therefore, this process enhances the performance of LDA for SSSP, and eventually the recognition performance. This strategy has been tested on the ORL and Yale face database. Experiment results show that this approach works much better than classical facial feature extraction methods.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961V (30 October 2009); doi: 10.1117/12.833038
Show Author Affiliations
Li Meng, Academy of Military Transportation (China)
Yong Cai, Academy of Military Transportation (China)
Yuanxing Li, Academy of Military Transportation (China)
Min Wang, Academy of Military Transportation (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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