Wavelet-based SVD method for face recognition with one training sample per person

Jiazhong He; Minghui Du

doi:10.1117/12.654912

3 November 2005 Wavelet-based SVD method for face recognition with one training sample per person

Jiazhong He, Minghui Du

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 60431C (2005) https://doi.org/10.1117/12.654912
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

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.

Citation Download Citation

Jiazhong He and Minghui Du "Wavelet-based SVD method for face recognition with one training sample per person", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60431C (3 November 2005); https://doi.org/10.1117/12.654912

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KEYWORDS

Facial recognition systems

Wavelets

Fourier transforms

Detection and tracking algorithms

Feature extraction

Databases

Matrices

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