计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2667-2670.DOI: 10.11772/j.issn.1001-9081.2013.09.2667

• 典型应用 • 上一篇    下一篇

完备正交邻域保持判别嵌入的人脸识别

陈达遥,陈伟琦,陈秀宏   

  1. 江南大学 数字媒体学院,江苏 无锡 214122
  • 收稿日期:2013-03-15 修回日期:2013-04-23 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 陈达遥
  • 作者简介:陈达遥(1988-),男,湖南益阳人,硕士研究生,主要研究方向:人工智能、模式识别;
    陈伟琦(1969-),男,江苏无锡人,工程师,主要研究方向:数字图像处理、计算机视觉;
    陈秀宏(1964-),男,江苏泰兴人,教授,博士,主要研究方向:数字图像处理、人脸识别。
  • 基金资助:

    国家自然科学基金资助项目;中央高校基本科研业务费专项资金资助项目

Face recognition based on complete orthogonal neighbourhood preserving discriminant embedding

CHEN Dayao,CHEN Weiqi,CHEN Xiuhong   

  1. School of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China
  • Received:2013-03-15 Revised:2013-04-23 Online:2013-10-18 Published:2013-09-01
  • Contact: CHEN Dayao

摘要: 为解决邻域保持判别嵌入算法所面临的小样本问题,并充分利用类内邻域散度矩阵零空间和非零空间中的判别信息进行人脸识别,提出一种完备正交邻域保持判别嵌入的人脸识别算法。首先间接地利用特征分解方法去除总体邻域散度矩阵的零空间;然后分别在类内邻域散度矩阵零空间和非零空间中提取最优判别矢量。此外,为进一步提高算法的识别性能,给出了基于瘦QR分解的正交投影矩阵的求解方法。在ORL和Yale人脸库上验证了以上算法的有效性。

关键词: 人脸识别, 特征提取, 零空间, 非零空间, 邻域保持判别嵌入

Abstract: In order to address Small Sample Size (SSS) problem encountered by Neighbourhood Preserving Discriminant Embedding (NPDE) and make full use of the discriminant information in the null space and non-null space of within-neighbourhood scatter matrix for face recognition, this paper proposed a Complete Orthogonal Neighbourhood Preserving Discriminant Embedding (CONPDE) algorithm for face recognition. The algorithm firstly removed the null space of the total neighbourhood scatter matrix using eigen decomposition method indirectly. Then, the optimal discriminant vectors were extracted in the null space and non-null space of within-neighbourhood scatter matrix, respectively. Besides, to further improve the recognition performance, the orthogonal projection matrix obtained based on economic QR decomposition was given. The experiments on ORL and Yale face database show the efficiency of the proposed method.

Key words: face recognition, feature extraction, null space, non-null space, Neighbourhood Preserving Discriminant Embedding (NPDE)

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