English

应用科学学报 ›› 2012, Vol. 30 ›› Issue (3): 281-286.doi: 10.3969/j.issn.0255-8297.2012.03.011

• 论文 • 上一篇    下一篇

人脸识别中的零范数稀疏编码

郎利影， 夏飞佳   

  1. 河北工程大学信息与电气工程学院，河北邯郸056038
  • 收稿日期:2010-09-12 修回日期:2011-05-17 出版日期:2012-05-30 发布日期:2012-05-30
  • 作者简介:郎利影，副教授，研究方向：信号处理及模式识别，E-mail：langliying@126.com
  • 基金资助:

    国家自然科学基金（No.60874116）资助

Zero-Norm Sparse Coding in Face Recognition

LANG Li-ying, XIA Fei-jia   

  1. College of Information and Electrical Engineering, Hebei University of Engineering,
    Handan 056038, Hebei Province, China  
  • Received:2010-09-12 Revised:2011-05-17 Online:2012-05-30 Published:2012-05-30

PDF

2394

被引次数

6

摘要：

为解决人脸识别中运算速度和识别效果之间的矛盾，提出了零范数稀疏编码算法. 该算法用零范数描述稀疏编码模型的稀疏度，通过对模型的间断点连续开拓，有效地提高了算法收敛速度. 运用ORL人脸数据库对该算法进行识别率和效率测试，并与非负稀疏编码算法和非负矩阵稀疏分解算法进行对比，表明文中提出的算法调节稀疏度的能力更强，可有效缩短运算时间，并在较短时间内获得较高的识别率.

关键词: 人脸识别, 稀疏编码, 稀疏度, 0 范数

Abstract:

To avoid conflict between algorithmic efficiency and recognition effectiveness in face recognition,this paper proposes a zero-norm sparse coding algorithm. The algorithm uses zero-norm to describe sparsity of a sparse coding model and applies a strategy of continuous extension of discontinuity points to speed convergence. A test based on the ORL database show that the algorithm is more efficient in adjusting sparsity so that the computation time is reduced, and gives higher recognition rate as compared with the methods of nonnegative sparse coding and non-negative matrix factorization with sparseness constraints.

Key words: face recognition, sparse coding, sparsity, zero-norm

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