Human authentication with finger textures based on image feature enhancement

R. R.O. Al-Nima; S. S. Dlay; W. L. Woo; J. A. Chambers

doi:10.1049/cp.2015.1784

Human authentication with finger textures based on image feature enhancement

R. R.O. Al-Nima, S. S. Dlay, W. L. Woo, J. A. Chambers

Computer and Information Sciences Department

Research output: Contribution to conference › Paper › peer-review

9 Citations (Scopus)

Abstract

The main goal of this paper is to authenticate people according to their finger textures. We propose to extract Finger Texture (FT) features of the four finger images (index, middle, ring and little) from a low resolution contactless hand image. Furthermore, we apply a new Image Feature Enhancement (IFE) method to enhance the FTs. The resulting feature image is segmented and a Probabilistic Neural Network (PNN) is employed as an intelligent classifier for recognition. Experimental results illustrate that the proposed approach has superior performance than recent published work. Moreover, the best IFE results were obtained with the Equal Error Rate (EER) equal to 4.07%.

Original language	English
DOIs	https://doi.org/10.1049/cp.2015.1784
Publication status	Published - 17 Nov 2016
Event	2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015 - London, United Kingdom Duration: 1 Dec 2015 → 2 Dec 2015

Conference

Conference	2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015
Country/Territory	United Kingdom
City	London
Period	1/12/15 → 2/12/15

Keywords

Biometric authentication
Finger texture
Image enhancement
Inner knuckles
PNN

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10.1049/cp.2015.1784

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@conference{ca3173435578444594b119fad2b74481,

title = "Human authentication with finger textures based on image feature enhancement",

abstract = "The main goal of this paper is to authenticate people according to their finger textures. We propose to extract Finger Texture (FT) features of the four finger images (index, middle, ring and little) from a low resolution contactless hand image. Furthermore, we apply a new Image Feature Enhancement (IFE) method to enhance the FTs. The resulting feature image is segmented and a Probabilistic Neural Network (PNN) is employed as an intelligent classifier for recognition. Experimental results illustrate that the proposed approach has superior performance than recent published work. Moreover, the best IFE results were obtained with the Equal Error Rate (EER) equal to 4.07%.",

keywords = "Biometric authentication, Finger texture, Image enhancement, Inner knuckles, PNN",

author = "Al-Nima, {R. R.O.} and Dlay, {S. S.} and Woo, {W. L.} and Chambers, {J. A.}",

year = "2016",

month = nov,

day = "17",

doi = "10.1049/cp.2015.1784",

language = "English",

note = "2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015 ; Conference date: 01-12-2015 Through 02-12-2015",

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TY - CONF

T1 - Human authentication with finger textures based on image feature enhancement

AU - Al-Nima, R. R.O.

AU - Dlay, S. S.

AU - Woo, W. L.

AU - Chambers, J. A.

PY - 2016/11/17

Y1 - 2016/11/17

N2 - The main goal of this paper is to authenticate people according to their finger textures. We propose to extract Finger Texture (FT) features of the four finger images (index, middle, ring and little) from a low resolution contactless hand image. Furthermore, we apply a new Image Feature Enhancement (IFE) method to enhance the FTs. The resulting feature image is segmented and a Probabilistic Neural Network (PNN) is employed as an intelligent classifier for recognition. Experimental results illustrate that the proposed approach has superior performance than recent published work. Moreover, the best IFE results were obtained with the Equal Error Rate (EER) equal to 4.07%.

AB - The main goal of this paper is to authenticate people according to their finger textures. We propose to extract Finger Texture (FT) features of the four finger images (index, middle, ring and little) from a low resolution contactless hand image. Furthermore, we apply a new Image Feature Enhancement (IFE) method to enhance the FTs. The resulting feature image is segmented and a Probabilistic Neural Network (PNN) is employed as an intelligent classifier for recognition. Experimental results illustrate that the proposed approach has superior performance than recent published work. Moreover, the best IFE results were obtained with the Equal Error Rate (EER) equal to 4.07%.

KW - Biometric authentication

KW - Finger texture

KW - Image enhancement

KW - Inner knuckles

KW - PNN

U2 - 10.1049/cp.2015.1784

DO - 10.1049/cp.2015.1784

M3 - Paper

AN - SCOPUS:85008368376

T2 - 2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015

Y2 - 1 December 2015 through 2 December 2015

ER -

Human authentication with finger textures based on image feature enhancement

Abstract

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Keywords

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