计算机工程与应用 ›› 2014, Vol. 50 ›› Issue (3): 141-144.

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局部非参数子空间分析在人脸识别中的应用

程  强,陈秀宏   

  1. 江南大学 数字媒体学院,江苏 无锡 214122
  • 出版日期:2014-02-01 发布日期:2014-01-26

Local nonparametric subspace analysis with applications to face recognition

CHENG Qiang, CHEN Xiuhong   

  1. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2014-02-01 Published:2014-01-26

摘要: 提出了一种局部非参数子空间分析算法(Local Nonparametric Subspace Analysis,LNSA),将其应用在人脸识别中。LNSA算法结合了非参数子空间算法(Nonparametric Subspace Analysis,NSA)与局部保留投影算法(Locality Preserving Projection,LPP)。它利用LPP算法中的相似度矩阵重构NSA的类内散度矩阵,使得在最大化类间散度矩阵的同时保留了类的局部结构。在ORL人脸库和XM2VTS人脸库上作了实验并证明LNSA方法要优于其他方法。

关键词: 人脸识别, 非参数子空间分析, 局部保留投影, 局部鉴别分析, 局部非参数子空间分析

Abstract: A local nonparametric subspace analysis algorithm is proposed and applied to face recognition. The algorithm, which combines nonparametric subspace analysis with locality preserving projection and reconstructs the within-class scatter matrix by the affinity matrix of locality preserving projection algorithm, makes it possible to maximize the between-class scatter matrix and meanwhile to preserve the class local structure.The experimental results on ORL and XM2VTS face databases show that the performance of local nonparametric discriminant analysis is better than other algorithms.

Key words: face recognition, nonparametric subspace analysis, locality preserving projection, local Fisher discriminant analysis, local nonparametric subspace analysis