| 基于2DLDA与SVM的人脸识别算法 |
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| 引用本文： | 甘俊英,何思斌.基于2DLDA与SVM的人脸识别算法[J].计算机应用,2009,29(7):1927-1929. |
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| 作者姓名： | 甘俊英 何思斌 |
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| 作者单位： | 五邑大学信息学院,广东江门,529020 |
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| 基金项目： | 广东省自然科学基金资助项目，广东省江门市科技攻关项目 |
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| 摘 要： | 二维线性鉴别分析（2DLDA）算法能有效解决线性鉴别分析（LDA）算法的“小样本”效应，支持向量机(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽略高频分量；然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM多类分类算法完成人脸识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。
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| 关 键 词： | 小波变换 二维LDA 支持向量机 人脸识别 |
| 收稿时间： | 2009-01-13 |
| 修稿时间： | 2009-03-10 |
Face recognition based on 2DLDA and SVM |
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| GAN Jun-ying,HE Si-bin.Face recognition based on 2DLDA and SVM[J].journal of Computer Applications,2009,29(7):1927-1929. |
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| Authors: | GAN Jun-ying HE Si-bin |
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| Affiliation: | School of Information;Wuyi University;Jiangmen Guangdong 529020;China |
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| Abstract: | "Small sample size" problem of LDA algorithm can be overcome by two-dimensional LDA(2DLDA),and Support Vector Machine(SVM) has the characteristic of structural risk minimization.In this paper,two methods were combined and used for face recognition.Firstly,the original images were decomposed into high-frequency and low-frequency components by Wavelet Transform(WT).The high-frequency components were ignored,while the low-frequency components can be obtained.Then,the liner discriminant features were extracted ... |
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