基于彩色人脸图像的信息融合与识别方法

An Information Fusion and Recognition Method for ■olor Face Images

图像的彩色信息进行图像识别并有效地降低因利用颜色信息所带来的计算量大幅增加问题,提出了一种基于彩色图像的监督近邻保留嵌套的人脸识别方法,通过对图像的彩色信息进行信息融合并利用监督近邻保留嵌套算法来提高人脸识别的效率。首先,采用Gabor变换分别对彩色图像的每个彩色分量图提取Gabor特征;然后采用典型相关分析对所提取的Gabor特征进行特征融合,并采用监督近邻保留嵌套算法对高维彩色图像特征进行降维;最后,采用最近邻分类器对图像进行分类。实验基于XM2VTS和FRAV2D彩色人脸数据库,采用主成分分析、线性判别分析以及监督近邻保留嵌套对基于灰度图像的Gabor特征和基于彩色信息融合的Gabor特征进行降维,其结果说明多信通彩色图像融合技术与监督近邻保留嵌套结合的方法可以显著提高识别系统性能。

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.