Face recognition system using ant colony optimization-based selected features

被引：30
|
作者：
Kanan, Hamidreza Rashidy
Faez, Karim
Hosseinzadeh, Mehdi
机构：
[1] Amirkabir Univ Technol, Dept Elect Engn, Image Proc & Pattern Recognit Lab, Tehran 15914, Iran
[2] Univ Isfahan, Fac Engn, Dept Comp Engn, Esfahan 81744, Iran
来源：
2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN SECURITY AND DEFENSE APPLICATIONS | 2007年
关键词：
D O I：
10.1109/CISDA.2007.368135
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Feature Selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm.
引用
收藏
页码：57 / 62
页数：6
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