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应用科学学报 ›› 2005, Vol. 23 ›› Issue (5): 497-501.

• 论文 • 上一篇    下一篇

基于SWA的核自联想记忆模型及其人脸识别应用

陈蕾1,2, 张道强2, 周鹏2, 陈松灿2   

  1. 1. 南京邮电大学计算机科学与技术系, 江苏南京 210003;
    2. 南京航空航天大学计算机科学与工程系, 江苏南京 210016
  • 收稿日期:2004-06-13 修回日期:2005-04-13 出版日期:2005-09-30 发布日期:2005-09-30
  • 作者简介:陈蕾(1975-),男,江西宜春人,硕士,E-mail:chenleijx@sohu.com;陈松灿(1962-),男,浙江余姚人,教授,博导,E-mail:s.chen@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金(60271017);江苏省自然科学基金(BK2002092)资助项目

Small-World Architecture Based Kernel Auto-Associative Memory Model and Its Application to Face Recognition

CHEN Lei1,2, ZHANG Dao-qiang2, ZHOU Peng2, CHEN Song-can2   

  1. 1. Department of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
    2. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2004-06-13 Revised:2005-04-13 Online:2005-09-30 Published:2005-09-30

PDF

78

被引次数

18

摘要： 通过在传统的自联想记忆模型中引入机器学习中颇具影响力的核方法,提出了一类囊括现有自联想记忆模型的统一的核自联想记忆模型框架(KAM),并针对KAM所具有的复杂的全互连结构,借鉴最近由Watts和Strogatz提出的"小世界网络"理论,构建了一类结构相对简单、易于硬件实现的基于小世界体系(SWA)的核自联想记忆模型框架(SWA-KAM).在FERET人脸数据库上的随机加噪和部分遮挡的识别实验表明,该模型获得了比PCA算法以及最近提出的(PC)2A算法更高的识别率,表现出了较强的鲁棒性.

关键词: 小世界体系, 联想记忆, 神经网络, 核方法, 人脸识别

Abstract: By introducing the kernel method into conventional auto-associative memory model (AM), a unified framework of kernel auto-associative memory model (KAM) is established, which extends the existing AM. Taking into account the complex full connectivity of KAM, and based on the small-world network described by Watts and Strogatz, this paper proposes a framework of small-world architecture based kernel auto-associative memory model (SWA-KAM), making VLSI implementation of AM easier.Simulation results on FERET face image database show that, SWA-KAM is more robust and has higher recognition rate than both PCA and (PC)2A algorithms in the presence of additive noise or partial occluding on face images.

Key words: small-world architecture (SWA), neural network, associative memory, kernel method, face recognition

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