Wavelet-based Training Sample Enhancement for Face Recognition with One Training Sample
Jiazhong He, Minghui Du · Computer Engineering and Applications Journal · 2006
At present,many methods can deal well with frontal view face recognition when there is sufficient number of representative training samples.However,the recognition performance of these methods decreases when only one training sample per person is available.In this paper,we propose an enhancement method of training sample based on Wavelet Transform Low-Frequency Band(WTLFB).In order to enhance the classification information of single training sample,each training sample is combined with its reconstructed image based on WTLFB into an enhanced sample.Then recognition is performed on a uniform eigen-space that obtained from Singular Value Decomposition(SVD) of the mean spectrum image of the enhanced training set.Experimental results show that on the Yale database where each person has only one training sample,the recognition accuracy of the proposed method is higher than the uniform eigen-space SVD method.