The traditional wavelet and Gabor wavelet cannot show facial features well in face recognition. In this paper

we propose a 2D dual-tree complex wavelet multi-frequency uncertainty weighted fusion for face recognition. 2D dual-tree complex wavelet multi-frequency features are used to show facial features. Weights and uncertainties are calculated to get the last facial feature by multi-frequency uncertainty weighted fusion algorithm. The weighted fusion algorithm first calculates the 2D DT-CWT multi-frequency filter images of the face

and then the uncertainty weights of the multi-frequency filters are calculated. Finally

the 2D DT-CWT multi-frequency filter is integrated into the last facial feature. At the same time

the 2D-PCA method is exploited to construct the linear subspace. The Euclidean distance based classifier is adopted for classification. Using the ORL database

the experimental results indicated that compared with the use of 2D-PCA

Wavelet

and Gabor wavelet feature extraction methods

the proposed method obtains a better recognition rate.