A robust method for the recognition of palmprints

O Nibouche; Hui Wang; Sriram Varadarajan; Bryan Scotney

doi:10.1109/AVSS.2017.8078487

A robust method for the recognition of palmprints

O Nibouche, Hui Wang, Sriram Varadarajan, Bryan Scotney

School of Electronics, Electrical Engineering and Computer Science

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

Abstract

Palmprint recognition has received in the last 20 years a great deal of the research community's attention. In this paper a new palmprint matching approach based on corner feature point extraction is proposed. A 72-element fixed-length descriptor is used to capture distinctive information of each feature point neighborhood and to build a measure of similarity whilst their coordinates provide a measure of proximity between the points. Matching two images takes into account both similarity and proximity measures which converts into a cost minimization problem. Our experiments carried out on a database of 250 prints from the Poly U database have yielded very good results evidenced by an EER of 0.31%.

Original language	English
Title of host publication	14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017: Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Print)	978-1-5386-2940-6
DOIs	https://doi.org/10.1109/AVSS.2017.8078487
Publication status	Published - 23 Oct 2017

Bibliographical note

14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) ; Conference date: 29-08-2017 Through 01-09-2017

Keywords

Feature extraction
Robustness
Databases
Coordinate measuring machines
Image resolution
Transforms
Eigenvalues and eigenfunctions

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10.1109/AVSS.2017.8078487Licence: Unspecified

Cite this

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title = "A robust method for the recognition of palmprints",

abstract = "Palmprint recognition has received in the last 20 years a great deal of the research community's attention. In this paper a new palmprint matching approach based on corner feature point extraction is proposed. A 72-element fixed-length descriptor is used to capture distinctive information of each feature point neighborhood and to build a measure of similarity whilst their coordinates provide a measure of proximity between the points. Matching two images takes into account both similarity and proximity measures which converts into a cost minimization problem. Our experiments carried out on a database of 250 prints from the Poly U database have yielded very good results evidenced by an EER of 0.31%.",

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author = "O Nibouche and Hui Wang and Sriram Varadarajan and Bryan Scotney",

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T1 - A robust method for the recognition of palmprints

AU - Nibouche, O

AU - Wang, Hui

AU - Varadarajan, Sriram

AU - Scotney, Bryan

N1 - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) ; Conference date: 29-08-2017 Through 01-09-2017

PY - 2017/10/23

Y1 - 2017/10/23

N2 - Palmprint recognition has received in the last 20 years a great deal of the research community's attention. In this paper a new palmprint matching approach based on corner feature point extraction is proposed. A 72-element fixed-length descriptor is used to capture distinctive information of each feature point neighborhood and to build a measure of similarity whilst their coordinates provide a measure of proximity between the points. Matching two images takes into account both similarity and proximity measures which converts into a cost minimization problem. Our experiments carried out on a database of 250 prints from the Poly U database have yielded very good results evidenced by an EER of 0.31%.

AB - Palmprint recognition has received in the last 20 years a great deal of the research community's attention. In this paper a new palmprint matching approach based on corner feature point extraction is proposed. A 72-element fixed-length descriptor is used to capture distinctive information of each feature point neighborhood and to build a measure of similarity whilst their coordinates provide a measure of proximity between the points. Matching two images takes into account both similarity and proximity measures which converts into a cost minimization problem. Our experiments carried out on a database of 250 prints from the Poly U database have yielded very good results evidenced by an EER of 0.31%.

KW - Feature extraction

KW - Robustness

KW - Databases

KW - Coordinate measuring machines

KW - Image resolution

KW - Transforms

KW - Eigenvalues and eigenfunctions

U2 - 10.1109/AVSS.2017.8078487

DO - 10.1109/AVSS.2017.8078487

M3 - Conference contribution

SN - 978-1-5386-2940-6

BT - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017: Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

A robust method for the recognition of palmprints

Abstract

Bibliographical note

Keywords

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Cite this