An Information Fusion and Recognition Method for Color Face Images
基于彩色人脸图像的信息融合与识别方法

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.