A face recognition system on distributed evolutionary computing using on-line GA

Paper

Young, Nam Mi, Bashar, Md. Rezaul and Rhee, Phill Kyu. 2006. "A face recognition system on distributed evolutionary computing using on-line GA." Huang, D., Li, K. and Irwin, G.W. (ed.) Intelligent Control and Automation: International Conference on Intelligent Computing ( ICIC 2006) Kunming, China, August, 2006. Kunming,China Aug 2006 Berlin / Heidelberg. https://doi.org/10.1007/978-3-540-37258-5_2
Paper/Presentation Title

A face recognition system on distributed evolutionary computing using on-line GA

Presentation TypePaper
AuthorsYoung, Nam Mi (Author), Bashar, Md. Rezaul (Author) and Rhee, Phill Kyu (Author)
EditorsHuang, D., Li, K. and Irwin, G.W.
Journal or Proceedings TitleLecture Notes in Control and Information Systems (Book series)
Journal Citation345, pp. 9-18
Number of Pages10
Year2006
Place of PublicationBerlin / Heidelberg
ISBN9783540372578
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-540-37258-5_2
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007%2F978-3-540-37258-5_2
Conference/EventIntelligent Control and Automation: International Conference on Intelligent Computing ( ICIC 2006) Kunming, China, August, 2006
Event Details
Intelligent Control and Automation: International Conference on Intelligent Computing ( ICIC 2006) Kunming, China, August, 2006
Event Date
Aug 2006
Event Location
Kunming,China
Abstract

Although there is much research on face recognition, there now exists some limitations especially in illumination and pose. This paper addresses a novel framework to prevail over the illumination barrier and a robust vision system. The key ideas of this paper are distributed evolutionary computing and on-line GA that is the combining concept of context-awareness and genetic algorithm. This research implements Fuzzy ART that carries out the context-awareness, modeling, and identification for the context environment and the system can also distinguish changing environments. On-line GA stores the experiences to make context knowledge that is used for on-line adaptation. Finally, supervised learning is applied to carry on recognition experiments. Experimental results on FERET data set show that On-line GA based face recognition performance is significantly benefited over the application of existing GA classification

Keywordsface recognition; genetic algorithms; image enhancement; image processing; robustness (control systems); context-aware filter fusion; context-awareness; filter fusion representation
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
460306. Image processing
Public Notes

ICIC 2006, LNCIS

Byline AffiliationsInha University, Korea
Book TitleIntelligent Computing in Signal Processing and Pattern Recognition
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