In this paper

a face recognition method

which utilizes an information fusion for color images and supervised neighbor preserving embedding

is presented for improving the perform ance of face recognition. First

Gabor transformation is used to extract information per channel of color image respectively

and then canonical correlation analysis is utilized to fuse extracted Gabor features. Supervised neighbor preserving embedding is used to reduce dimensionality. Finally

nearest neighbor classifier is used to classify reduced features. Experiments are carried on XM2VTS and FRAV2D color face databases

and utilize principal component analysis

linear discriminant analysis and supervised neighbor preserving embedding to reduce dimensionality of Gabor features on gray method and multi-channel feature fusion method. These results show that the combination of multi-channel information fusion and supervised neighbor preserving embedding can improve the performance of recognition system.