Palmprint recognition using valley features

Xiang Qian Wu; Kuan Quan Wang; Dapeng Zhang

Palmprint recognition using valley features

Xiang Qian Wu, Kuan Quan Wang, Dapeng Zhang

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

11 Citations (Scopus)

Abstract

This paper presents a novel approach for palmprint recognition based on the valley features. This approach uses the bothat operation to extract the valleys from a very low-resolution palm image in different directions to form the valley feature, and then define a matching score to measure the similarity of the valley features. The experimental results shows that the proposed approach can effectively discriminate palmprints and can obtain about 98% accuracy in palmprint verification.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	4881-4885
Number of pages	5
Publication status	Published - 12 Dec 2005
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Biometrics
Morphological operator
Palmprint recognition
Valley feature

ASJC Scopus subject areas

General Engineering

More information

http://hdl.handle.net/10397/1184

Cite this

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title = "Palmprint recognition using valley features",

abstract = "This paper presents a novel approach for palmprint recognition based on the valley features. This approach uses the bothat operation to extract the valleys from a very low-resolution palm image in different directions to form the valley feature, and then define a matching score to measure the similarity of the valley features. The experimental results shows that the proposed approach can effectively discriminate palmprints and can obtain about 98% accuracy in palmprint verification.",

keywords = "Biometrics, Morphological operator, Palmprint recognition, Valley feature",

author = "Wu, {Xiang Qian} and Wang, {Kuan Quan} and Dapeng Zhang",

year = "2005",

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day = "12",

language = "English",

isbn = "078039092X",

pages = "4881--4885",

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TY - GEN

T1 - Palmprint recognition using valley features

AU - Wu, Xiang Qian

AU - Wang, Kuan Quan

AU - Zhang, Dapeng

PY - 2005/12/12

Y1 - 2005/12/12

N2 - This paper presents a novel approach for palmprint recognition based on the valley features. This approach uses the bothat operation to extract the valleys from a very low-resolution palm image in different directions to form the valley feature, and then define a matching score to measure the similarity of the valley features. The experimental results shows that the proposed approach can effectively discriminate palmprints and can obtain about 98% accuracy in palmprint verification.

AB - This paper presents a novel approach for palmprint recognition based on the valley features. This approach uses the bothat operation to extract the valleys from a very low-resolution palm image in different directions to form the valley feature, and then define a matching score to measure the similarity of the valley features. The experimental results shows that the proposed approach can effectively discriminate palmprints and can obtain about 98% accuracy in palmprint verification.

KW - Biometrics

KW - Morphological operator

KW - Palmprint recognition

KW - Valley feature

UR - http://www.scopus.com/inward/record.url?scp=28444454517&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SN - 078039092X

SN - 9780780390928

SP - 4881

EP - 4885

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

Palmprint recognition using valley features

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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