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拉普拉斯二维主成分分析及其在人脸识别中的应用

Laplacian's Two-dimensional Principal Component Analysis and Its Application to Face Recognition
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摘要 在二维主成分分析的基础上,考虑样本的流形分布特点,引入样本相似系数,重新定义了样本拉普拉斯散布矩阵,进而给出了基于拉普拉斯二维主成分分析的特征提取方法.在ORL,FERET人脸库上的试验证明了基于拉普拉斯二维主成分分析方法的有效性. Based on two-dimensional principal component analysis, this paper investigates the features of manifold distrihution. Sample similarity coefficient is introduced to redefine Laplaeian scattering matrix, and thus a feature extraction method of this kind is worked out. The results of the experimenls conducted on ORL and FERET face database indieate that the method is effective.
作者 黄赛平 杨万扣 黄陈蓉
机构地区 南京工程学院计算机工程学院 南京理工大学计算机科学与技术学院
出处 《南京工程学院学报（自然科学版）》 2009年第4期55-59,共5页 Journal of Nanjing Institute of Technology(Natural Science Edition)
基金 江苏省自然科学基金(BK2009352)
关键词 二维主成分分析 拉普拉斯 特征抽取 人脸识别 two-dimensional principal component analysis Laplacian feature extraction face recognition
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献11

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二级参考文献78

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共引文献176

南京工程学院学报（自然科学版）
2009年 第4期

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