| 结合DCT与KPCA的人脸识别 |
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| 引用本文： | 刘嵩.结合DCT与KPCA的人脸识别[J].计算机工程与应用,2012,48(27):186-188,205. |
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| 作者姓名： | 刘嵩 |
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| 作者单位： | 湖北民族学院信息工程学院,湖北恩施445000;华中师范大学物理科学与技术学院,武汉430079 |
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| 基金项目： | 湖北省自然科学基金(No.2009CDB069) |
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| 摘 要： | 核主成分分析是主成分分析在核空间中的非线性推广,能有效应用于人脸识别,但是识别过程时间开销过大仍是待解决的问题。提出了一种结合离散余弦变换和核主分量分析的人脸识别方法。对人脸图像进行离散余弦变换,选择部分系数重建图像,采用核主分量分析的方法提取人脸特征,采用最近邻分类器进行识别。在ORL人脸库上的仿真结果表明所提出的方法速度快,综合性能优于核主成分分析方法。
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| 关 键 词： | 人脸识别 特征提取 核主成分分析 离散余弦变换 最近邻分类器 |
Face recognition based on DCT and KPCA |
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| LIU Song.Face recognition based on DCT and KPCA[J].Computer Engineering and Applications,2012,48(27):186-188,205. |
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| Authors: | LIU Song |
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| Affiliation: | LIU Song 1.College of Information Engineering,Hubei Institute for Nationalities,Enshi,Hubei 445000,China 2.College of Physical Science and Technology,Huazhong Normal University,Wuhan 430079,China |
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| Abstract: | As the nonlinear extensions of Principal Component Analysis(PCA),Kernel Principal Component Analysis(KPCA)is effective for face recognition.In order to reduce recognition time,a face recognition method based on Discrete Cosine Transform(DCT)and KPCA is presented.The feature coefficients are extracted by DCT,and part of the coefficients are chosen to reconstruct face images.The face feature of high dimention is extracted by KPCA.The nearest neighbor classifier is used for identification.The experiment result on ORL face databases shows this method has the property of being faster,and the comprehensive performance is better than that of KPCA. |
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| Keywords: | face recognition feature extract Kernel Principle Component Analysis(KPCA) Discrete Cosine Transform(DCT) nearest neighbor classifier |
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