Please use this identifier to cite or link to this item:
Title: Authentication of individuals using hand geometry biometrics: A neural network approach
Authors: Faundez-Zanuy, Marcos
Elizondo, David A.
Ferrer-Ballester, Miguel Ángel 
Travieso-González, Carlos M. 
Keywords: Biometrics
Hand geometry
Biometrical features
Feature extraction
Feature identification, et al
Issue Date: 2007
Publisher: 1370-4621
Journal: Neural Processing Letters 
Abstract: Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.
ISSN: 1370-4621
DOI: 10.1007/s11063-007-9052-y
Source: Neural Processing Letters[ISSN 1370-4621],v. 26, p. 201-216
Appears in Collections:Artículos
Adobe PDF (521,43 kB)
Show full item record


checked on Sep 26, 2021


checked on Sep 26, 2021

Page view(s)

checked on Jul 31, 2021


checked on Jul 31, 2021

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.