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Proceedings Paper

Wavelet-based SVD method for face recognition with one training sample per person
Author(s): Jiazhong He; Minghui Du
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Paper Abstract

At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. However, few of them can work well when only one training sample per class is available. In this paper, we present a method of face recognition based on wavelet low-frequency band and singular value decomposition (SVD) to solve the one training sample problem. To acquire more information from the single training sample, training image is linearly combined with its reconstructed image of wavelet low-frequency band into a new training image. By using Fourier transform, the spectrum representation of face image is obtained that is invariant against spatial translation. Then the spectrum representation is projected into a uniform eigen-space that is obtained from SVD of standard face image and the coefficient matrix is used as feature for recognition. The proposed algorithm obtains acceptable experimental results on the ORL face database.

Paper Details

Date Published: 3 November 2005
PDF: 7 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60431C (3 November 2005); doi: 10.1117/12.654912
Show Author Affiliations
Jiazhong He, South China Univ. of Technology (China)
Shaoguan College (China)
Minghui Du, South China Univ. of Technology (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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