Biometric Identification through Hand Geometry

Author

Hashemi, Javad ; Fatemizadeh, Emad

Author_Institution

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

Volume

2

fYear

2005

fDate

21-24 Nov. 2005

Firstpage

1011

Lastpage

1014

Abstract

A new approach for person identification based on hand geometry is presented. After preprocessing hand features are extracted from a photograph taken while user has placed his/her hand (either left or right) on the platform of a document scanner with no limits or fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), radial basis function neural networks (RBF), multilayer perceptron (MLP), k-nearest neighbor (k-NN), Bayes method and Mahalanobis/Hamming distance have been used in classification section. Experimental results show a rate of success above 90%

Keywords

Bayes methods; Gaussian processes; biometrics (access control); feature extraction; image recognition; multilayer perceptrons; pattern classification; radial basis function networks; Bayes method; Gaussian mixture modeling; Hamming distance; Mahalanobis distance; biometric identification; document scanner; hand feature extraction; hand geometry; image classification; k-nearest neighbor; multilayer perceptron; pattern recognition; person identification; radial basis function neural networks; Biometrics; Costs; Feature extraction; Fingerprint recognition; Fingers; Geometry; Image databases; Iris; Java; Retina; Biometric; GMMs; Hand geometry; RBF neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer as a Tool, 2005. EUROCON 2005.The International Conference on

Conference_Location

Belgrade

Print_ISBN

1-4244-0049-X

Type

conf

DOI

10.1109/EURCON.2005.1630119

Filename

1630119