摘要
论文提出了一种基于Gabor小波和二维主元分析(2DPCA)的人脸识别方法。该方法首先对人脸图像进行Gabor小波变换,将小波变换的系数作为人脸图像的特征向量;然后,用2DPCA对所得的人脸图像特征进行降维,并采用最近邻法进行分类;最后,利用AT&T人脸库,对基于Gabor小波和二维主元分析(2DPCA)的人脸识别方法和基于Gabor小波和PCA的人脸识别方法进行了仿真比较实验。仿真实验表明,基于Gabor小波和2DPCA的人脸识别方法具有较好的识别性能。
This paper presents a method of face recognition based on the Gabor wavelet and two-dimensional principal component analysis （2DPCA ）.First , the coefficients of Gabor wavelet transform deriving from a face image are taken as eigenvectors.And then 2DPCA is used to decrease the dimension of the eigenvector,and the nearest neighbor classifier is employed for face classification.Finally,by use of the AT＆T face database,the comparison simulations are performed both on the method based on the Gabor wavelet and 2DPCA,and the one based on the Gabor wavelet and PCA.The simulation result shows that the former has the good recognition performance for the face image.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第10期55-57,共3页
Computer Engineering and Applications
基金
河南省科技厅资助项目
河南省教委自然科学基金资助项目(编号:2003120015)
河南省高校创新人才工程项目
