Face Recognition System Using Ant Colony Optimization-Based Selected Features

Author

Kanan, Hamidreza Rashidy ; Faez, Karim ; Hosseinzadeh, Mehdi

Author_Institution

Electr. Eng. Dept., Amirkabir Univ. of Technol., Tehran

fYear

2007

fDate

1-5 April 2007

Firstpage

57

Lastpage

62

Abstract

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

Keywords

face recognition; feature extraction; optimisation; search problems; visual databases; ORL database; ant colony optimization; face recognition system; feature selection; heuristic information; pattern recognition system; Ant colony optimization; Application software; Artificial intelligence; Computational intelligence; Computer security; Discrete wavelet transforms; Face recognition; Image processing; Particle swarm optimization; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Security and Defense Applications, 2007. CISDA 2007. IEEE Symposium on

Conference_Location

Honolulu, HI

Print_ISBN

1-4244-0700-1

Type

conf

DOI

10.1109/CISDA.2007.368135

Filename

4219082