Biometric recognition using entropy-based discretization

Ajay Kumar Pathak; Dapeng Zhang

doi:10.1109/ICASSP.2007.366188

Biometric recognition using entropy-based discretization

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The biometrics based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature level representation. This paper suggests and investigates the performance improvement of the existing biometric systems using the discretization of extracted features. The performance improvement due to the unsupervised and supervised discretization schemes is compared on verity of classifiers; KNN, naïve Bayes, SVM and FFN. The experimental results on the hand-geometry database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in biometrics systems.

Original language	English
Title of host publication	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Volume	2
DOIs	https://doi.org/10.1109/ICASSP.2007.366188
Publication status	Published - 6 Aug 2007
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: 15 Apr 2007 → 20 Apr 2007

Conference

Conference	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	15/04/07 → 20/04/07

Keywords

Biometrics
Feature discretization
Feature representation
Hand geometry
Personal recognition

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

More information

10.1109/ICASSP.2007.366188

http://hdl.handle.net/10397/205

Cite this

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title = "Biometric recognition using entropy-based discretization",

abstract = "The biometrics based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature level representation. This paper suggests and investigates the performance improvement of the existing biometric systems using the discretization of extracted features. The performance improvement due to the unsupervised and supervised discretization schemes is compared on verity of classifiers; KNN, na{\"i}ve Bayes, SVM and FFN. The experimental results on the hand-geometry database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in biometrics systems.",

keywords = "Biometrics, Feature discretization, Feature representation, Hand geometry, Personal recognition",

author = "Pathak, {Ajay Kumar} and Dapeng Zhang",

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AB - The biometrics based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature level representation. This paper suggests and investigates the performance improvement of the existing biometric systems using the discretization of extracted features. The performance improvement due to the unsupervised and supervised discretization schemes is compared on verity of classifiers; KNN, naïve Bayes, SVM and FFN. The experimental results on the hand-geometry database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in biometrics systems.

KW - Biometrics

KW - Feature discretization

KW - Feature representation

KW - Hand geometry

KW - Personal recognition

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DO - 10.1109/ICASSP.2007.366188

M3 - Conference article published in proceeding or book

SN - 1424407281

SN - 9781424407286

VL - 2

BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

Y2 - 15 April 2007 through 20 April 2007

ER -

Biometric recognition using entropy-based discretization

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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