摘要
提出了一种对角离散余弦变换(Discrete Cosine Transform,DCT)和二维主元分析(Two-Dimensional Principal Component Analysis,2DPCA)相结合的人脸识别方法。该算法首先将人脸图像转换成对角图像,同时利用DCT压缩并重建人脸图像;然后通过2DPCA进行特征提取得到人脸识别特征;最后运用最近邻分类器进行识别。基于ORL(Olivetti Research Laboratory)、受污损ORL及Yale人脸数据库的实验结果证明了该算法的有效性。
Combined with Discrete Cosine Transform（DCT） and Two-Dimensional Principal Component Analysis（2DPCA）,a novel method in face recognition was presented in this paper.Firstly,face image is turned into diagonal image,and dimension is reduced by DCT.Tben image is reconstructed by IDCT.Secondly,2DPCA is used in feature extraction,and face recognition features are obtained.Finally,the Nearesi Neigbbor（NN） classifier is selected to perform face recognition,Experimental results on ORL（Olivetti Research Laboratory）,damaged ORL and Yale face database show that the method is efficient in face recognition.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第31期210-213,共4页
Computer Engineering and Applications
基金
广东省自然科学基金(the Natural Science Foundation of Guangdong Province of China under Grant No.032356)
北京大学视觉与听觉信息处理国家重点实验室开放课题基金项目(No.0505)
关键词
离散余弦变换
二维主元分析
图像重建
人脸识别
Discrete Cosine Transform（DCT）
Two-Dimensional Principal Component Analysis （2DPCA）
image reconstruction
face recognition
