Wavelet-based SVD method for face recognition with one training sample per person
Jiazhong He, Minghui Du · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.