An automated palmprint recognition system

Tee Connie; Andrew Teoh Beng Jin; Michael Goh Kah Ong; David Ngo Chek Ling

doi:10.1016/j.imavis.2005.01.002

An automated palmprint recognition system

Tee Connie, Andrew Teoh Beng Jin, Michael Goh Kah Ong, David Ngo Chek Ling

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

224 Citations (Scopus)

Abstract

Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multi-frequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database.

Original language	English
Pages (from-to)	501-515
Number of pages	15
Journal	Image and Vision Computing
Volume	23
Issue number	5
DOIs	https://doi.org/10.1016/j.imavis.2005.01.002
Publication status	Published - 2005 May 1

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Vision and Pattern Recognition

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10.1016/j.imavis.2005.01.002

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title = "An automated palmprint recognition system",

abstract = "Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multi-frequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database.",

author = "Tee Connie and Jin, {Andrew Teoh Beng} and Ong, {Michael Goh Kah} and Ling, {David Ngo Chek}",

year = "2005",

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T1 - An automated palmprint recognition system

AU - Connie, Tee

AU - Jin, Andrew Teoh Beng

AU - Ong, Michael Goh Kah

AU - Ling, David Ngo Chek

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N2 - Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multi-frequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database.

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An automated palmprint recognition system

Abstract

All Science Journal Classification (ASJC) codes

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