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
Other Authors: Dr. Muhammad Imran Ahmad
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2019
Subjects:
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61982
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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 Palmprints DSP processor Human identification system Biometric technology Human identification 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. 2019-09-25T03:35:54Z 2019-09-25T03:35:54Z 2015 Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61982 en Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
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.
author2 Dr. Muhammad Imran Ahmad
author_facet Dr. Muhammad Imran Ahmad
Thulfiqar Hussein, Mandeel
format Thesis
author 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
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2019
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61982
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score 13.214268