Effective Feature Selection for Face Recognition Based on Correspondence Analysis and Trained Artificial Neural Network

Author

Pazoki, Zohreh ; Farokhi, Fardad

Author_Institution

Sci. Assoc. of Electr. & Electron. Eng., Islamic Azad Univ. Central Tehran Branch, Tehran, Iran

fYear

2010

fDate

15-18 Dec. 2010

Firstpage

80

Lastpage

84

Abstract

This paper presents a face recognition method based on correspondence analysis (CA) and trained artificial neural network. In this algorithm, features are extracted using CA, then these features are fed to Multi layer Perceptron (MLP)network for classification and finally, after training the network, effective features are selected with UTA algorithm. The obtained experimental results indicate high average accuracy (98%) and the minimum run time of the algorithm as well.

Keywords

face recognition; feature extraction; image classification; multilayer perceptrons; correspondence analysis; face recognition; feature extraction; feature selection; image classification; multilayer perceptron network; trained artificial neural network; Accuracy; Algorithm design and analysis; Artificial neural networks; Classification algorithms; Face; Face recognition; Feature extraction; Correspondence Analysis (CA); Multilayer perceptron (MLP) neural network; UTA algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal-Image Technology and Internet-Based Systems (SITIS), 2010 Sixth International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-9527-6

Electronic_ISBN

978-0-7695-4319-2

Type

conf

DOI

10.1109/SITIS.2010.23

Filename

5714533