Abstract: A feature fusing algorithm based on kernel canonical correlation analysis KCCA) was constructed in this paper.First,the image data are mapped through kernel function,and then two groups of feature vectors with the same pattern are extracted and the correlation criterion function between the two groups of feature vectors are established.Se-condly,two groups of canonical projective vectors are extracted according to this function.Thirdly,feature fusion for classification is done by using proposed strategy.The advantage of the proposed algorithm lies in the following aspects.Firstly,it suits for information fusion.Secondly,it eliminates the redundant information within the features,and it simplifies the computation without decomposing the mapped matrix and gains more discriminated features.The results of experiments on AR face database,PIE face database,ORL face database,Yale face database and UCI handwritten digit database show that this algorithm is efficient and robust.

Key words: Kernel function,Kernel canonical correlation analysis,Feature fusion,Combined feature extraction,Face recognition