基于分块统计量的Gabor特征描述方法及人脸识别

Block Statistics Based Gabor Feature Representation and Its Application to Face Recognition

Gabor 小波是人脸特征描述中的一个重要工具.为减少由直接对 Gabor 特征进行下采样造成的有用信息丢失,本文提出一种基于分块统计量的 Gabor 特征描述方法,增强人脸图像的 Gabor 特征描述效率.在此基础上,探讨基于广义鉴别分析的二次特征提取方法.实验表明,Gabor 特征描述和广义鉴别分析两种方法结合后所产生的识别性能优于其中每个方法单独使用的识别性能,且与 Eigenfaces、Fisherfaces 等流行方法相比具有较大优势.

Face representation based on Gabor features has attracted much attention and achieved great success in face recognition for some favorable attributes of Gabor wavelets such as spatial locality and orientation selectivity. A large number of Gabor features are produced with varying parameters in the position, scale and orientation of filters. In some existing methods, useful discriminatory information may be lost due to down-sampling Gabor features directly. To reduce the loss, a block statistics based Gabor feature representation method is proposed. The effectiveness of this method is demonstrated by template matching test on ORL face database, and the comparative test results show that this method can yield better recognition accuracy with much fewer Gabor features as well as less CPU time of feature matching than the existing approach of down-sampling based Gabor feature representation. In addition, Generalized Discriminant Analysis （GDA） which performs dimensionality reduction to Gabor features is used to produce more compact and discriminatory face representation. The experimental results of face recognition using different similarity measures show that the proposed method outperforms the famous Eigenfaces and Fisherfaces methods significantly, and the rationality of this combination is also comparatively demonstrated.