Palmprint recognition using valley features

Xiang Qian Wu; Kuan Quan Wang; David Zhang

Palmprint recognition using valley features

Xiang Qian Wu^*
, Kuan Quan Wang
, David Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

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
State	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 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

Cite this

@inproceedings{a55938a5c5104d71bef6bf1b62d689ae,

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 David Zhang",

year = "2005",

language = "英语",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "4881--4885",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

Palmprint recognition using valley features. / Wu, Xiang Qian ; Wang, Kuan Quan; Zhang, David.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 4881-4885 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Palmprint recognition using valley features

AU - Wu, Xiang Qian

AU - Wang, Kuan Quan

AU - Zhang, David

PY - 2005

Y1 - 2005

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 - https://www.scopus.com/pages/publications/28444454517

M3 - 会议稿件

AN - SCOPUS:28444454517

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

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

Publication series

Conference

Keywords

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Cite this