基于SWA的核自联想记忆模型及其人脸识别应用

Journal of Applied Sciences ›› 2005, Vol. 23 ›› Issue (5): 497-501.

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

CHEN Lei^1,2, ZHANG Dao-qiang², ZHOU Peng², CHEN Song-can²

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

Abstract

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

CLC Number:

TP183

CHEN Lei, ZHANG Dao-qiang, ZHOU Peng, CHEN Song-can. Small-World Architecture Based Kernel Auto-Associative Memory Model and Its Application to Face Recognition[J]. Journal of Applied Sciences, 2005, 23(5): 497-501.

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

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