Obtain appropriate low-dimension face features is an important problem in the area of face recognition. Traditional face recognition algorithms based on wavelet transform extract image features using only the low frequency components for classification

which results in the loss of information

which could be used for face recognition. In order to effectively extract the face image features

a new algorithm of face recognition based on wavelet transform and weighted fusion of features is proposed in this study. First

the wavelet transform is used to reduce the dimensionality; then

the features of the four wavelet sub-graphs are extracted by a principal component analysis (PCA)

and the features of the four parts are fused into discriminant features. Finally

the features are classified and recognized by SVM. Experimental results on the ORL face database show that the proposed algorithm achieves a recognition accuracy of 97.5 percent

so the new algorithm can effectively improve the face recognition ability. It has a higher recognition accuracy than traditional methods.