A robust method for the recognition of palmprints

O Nibouche; H. Wang; Sriram Varadarajan; Bryan Scotney

A robust method for the recognition of palmprints

O Nibouche
, H. Wang
, Sriram Varadarajan
, Bryan Scotney

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

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
ISBN (Electronic)	978-1-5386-2939-0
Publication status	Published (in print/issue) - 29 Aug 2017
Event	14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - Lecce, Italy Duration: 29 Aug 2017 → 1 Sept 2017 https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding

Conference

Conference	14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
City	Italy
Period	29/08/17 → 1/09/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

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

Access to Document

https://ieeexplore.ieee.org/document/8078487

Cite this

@inproceedings{fcdc94e6ed22412785aa4001371a81c5,

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\%.",

keywords = "Feature extraction, Robustness, Databases, Coordinate measuring machines, Image resolution, Transforms, Eigenvalues and eigenfunctions",

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

year = "2017",

month = aug,

day = "29",

language = "English",

isbn = "978-1-5386-2940-6",

booktitle = "14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017",

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

url = "https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding",

}

TY - GEN

T1 - A robust method for the recognition of palmprints

AU - Nibouche, O

AU - Wang, H.

AU - Varadarajan, Sriram

AU - Scotney, Bryan

PY - 2017/8/29

Y1 - 2017/8/29

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

UR - https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding

M3 - Conference contribution

SN - 978-1-5386-2940-6

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

T2 - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Y2 - 29 August 2017 through 1 September 2017

ER -

A robust method for the recognition of palmprints

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this