Palmprint recognition using eigen-palm image implemented on DSP processor

This study focuses on the development of a human identification system using eigenpalm images. Human identification based on biometric technology is extensively used in several applications, such as access control and criminal investigation. The proposed method consists of three main stages. The...

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Main Author:	Thulfiqar Hussein, Mandeel
Format:	Thesis
Language:	English
Subjects:	Palmprints DSP processor Human identification system Biometric technology Human identification
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/2/Full%20text.pdf
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id	my-unimap-61982
record_format	uketd_dc
spelling	my-unimap-619822019-09-25T03:35:54Z Palmprint recognition using eigen-palm image implemented on DSP processor Thulfiqar Hussein, Mandeel Dr. Muhammad Imran Ahmad This study focuses on the development of a human identification system using eigenpalm images. Human identification based on biometric technology is extensively used in several applications, such as access control and criminal investigation. The proposed method consists of three main stages. The preprocessing stage computes the palmprint images to capture important information and produce a better representation of palmprint image data. The second stage extracts significant features from palmprint images and reduces the dimension of the palmprint image data by applying the principal component analysis (PCA) technique. A linear projection method is used in this stage to reduce redundant features and remove noise from the palmprint image. Furthermore, this approach increases discrimination power in the feature space. The Euclidean distance classifier is used in the classification stage, which is the third stage. The proposed method is tested using a benchmark PolyU dataset. Experimental results show that the best achieved recognition rate is 97.5% when the palmprint image is resized with 0.2 resizing scale and represented using 34 PCA coefficients. The raw data projection and Euclidean distance classifier can be implemented on a digital signal processor (DSP) board. Implementing the proposed algorithm using the DSP board achieves better performance in computation time compared with a personal computerbased system which make the system 47.2% faster. Universiti Malaysia Perlis (UniMAP) 2015 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61982 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/1/Page%201-24.pdf e7d929efc025ad212873c225cedf5c11 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/2/Full%20text.pdf a9ab96d474412d5a71c177be8db0d755 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Palmprints DSP processor Human identification system Biometric technology Human identification School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Muhammad Imran Ahmad
topic	Palmprints DSP processor Human identification system Biometric technology Human identification
spellingShingle	Palmprints DSP processor Human identification system Biometric technology Human identification Thulfiqar Hussein, Mandeel Palmprint recognition using eigen-palm image implemented on DSP processor
description	This study focuses on the development of a human identification system using eigenpalm images. Human identification based on biometric technology is extensively used in several applications, such as access control and criminal investigation. The proposed method consists of three main stages. The preprocessing stage computes the palmprint images to capture important information and produce a better representation of palmprint image data. The second stage extracts significant features from palmprint images and reduces the dimension of the palmprint image data by applying the principal component analysis (PCA) technique. A linear projection method is used in this stage to reduce redundant features and remove noise from the palmprint image. Furthermore, this approach increases discrimination power in the feature space. The Euclidean distance classifier is used in the classification stage, which is the third stage. The proposed method is tested using a benchmark PolyU dataset. Experimental results show that the best achieved recognition rate is 97.5% when the palmprint image is resized with 0.2 resizing scale and represented using 34 PCA coefficients. The raw data projection and Euclidean distance classifier can be implemented on a digital signal processor (DSP) board. Implementing the proposed algorithm using the DSP board achieves better performance in computation time compared with a personal computerbased system which make the system 47.2% faster.
format	Thesis
author	Thulfiqar Hussein, Mandeel
author_facet	Thulfiqar Hussein, Mandeel
author_sort	Thulfiqar Hussein, Mandeel
title	Palmprint recognition using eigen-palm image implemented on DSP processor
title_short	Palmprint recognition using eigen-palm image implemented on DSP processor
title_full	Palmprint recognition using eigen-palm image implemented on DSP processor
title_fullStr	Palmprint recognition using eigen-palm image implemented on DSP processor
title_full_unstemmed	Palmprint recognition using eigen-palm image implemented on DSP processor
title_sort	palmprint recognition using eigen-palm image implemented on dsp processor
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61982/2/Full%20text.pdf
_version_	1747836850523865088

Palmprint recognition using eigen-palm image implemented on DSP processor

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