关键词: 小波变换, 图像融合, 主成分分析（PCA）, 非负矩阵分解（NMF）

Abstract: A new fusion of PCA and NMF combined with image fusing for face recognition method is proposed.The original images are decomposed into high-frequency and low-frequency components by wavelet transform.PCA is used to subtract the features of the lowest-frequency subimage.To the others，although they contain less energy than lowest-frequency subimage，they contain important information about classification.NMF is used to subtract the features of the fusing image.The nearest neighbor classifier is used to classification.And fusing the classification with weight.Experimental results show the method can improve face recognition rate.

Key words: wavelet transform, image fusion, Principal Component Analysis（PCA）, Non-negative Matrix Factorization（NMF）