Fusion of holistic and local features for palmprint recognition

Palmprint recognition has become an important and rapidly developing technology in biometric system over the past decade. The success of palmprint identification requires the best matching of the test sample from input data and the templates in the palmprint database. The used of holistic and loc...

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Format:	Thesis
Language:	English
Subjects:	Biometric identification Palmprints Principal components analysis
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/3/Declaration%20Form.pdf
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id	my-unimap-76738
record_format	uketd_dc
spelling	my-unimap-767382022-11-08T03:06:39Z Fusion of holistic and local features for palmprint recognition Muhammad Amran, Ahmad, Dr. Palmprint recognition has become an important and rapidly developing technology in biometric system over the past decade. The success of palmprint identification requires the best matching of the test sample from input data and the templates in the palmprint database. The used of holistic and local features separately will have limitations in geometry and variations. Information fusion of palmprint biometric is developed in order to produce a better recognition performance. Thus, this research work presented the fusion of holistic and local features for palmprint recognition. The overall structure of the study takes the form of three major steps includes pre-processing techniques, feature extraction and matching process before proceeds with fusion to combine the holistic and local features. Pre-processing technique is the initial stage to make sure the palmprint image from dataset is cropped and resized into the specific size. The entire proposed method is validated using benchmark PolyU dataset for palmprint recognition analysis. Gabor filter pattern is used to extract important information in holistic features while Discrete Cosine Transform (DCT) is used to extract low frequency energy of local features. Then, the computation of Principal Component Analysis (PCA) is applied to reduce the high dimensional feature space to low dimensional feature space. Low dimensional feature space preserved low frequency information. Classification is the process used to distinguish and classify a new observation based on the training set of data. The purpose of Euclidean distance classifier is to measure the matching value and its closeness between the training and testing feature vectors. Among various fusion levels, matching score level fusion is the most suitable approach in combining the match score from two different matchers because this fusion rule can increase matching accuracy. It is developed by forming a single value for decision process from the matching output of different matching module. In the matching score fusion scheme, weighted sum rule produced superior performance. The best recognition rate of 97% is achieved using 100 subjects. Based on the recognition analysis, there are three important parameters that affect the performance which is the size of input image, the effect of principal components number, and the number of DCT coefficient. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76738 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/4/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/1/Page%201-24.pdf a9fe41d8040e76ab18bf485bb2684ff6 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/2/Full%20text.pdf e5af3c5fd84ae3b1b7e8df9148964481 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/3/Declaration%20Form.pdf 72230c4b2661346d672f2e0b69fd6999 Universiti Malaysia Perlis (UniMAP) Biometric identification Palmprints Principal components analysis School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Muhammad Amran, Ahmad, Dr.
topic	Biometric identification Palmprints Principal components analysis
spellingShingle	Biometric identification Palmprints Principal components analysis Fusion of holistic and local features for palmprint recognition
description	Palmprint recognition has become an important and rapidly developing technology in biometric system over the past decade. The success of palmprint identification requires the best matching of the test sample from input data and the templates in the palmprint database. The used of holistic and local features separately will have limitations in geometry and variations. Information fusion of palmprint biometric is developed in order to produce a better recognition performance. Thus, this research work presented the fusion of holistic and local features for palmprint recognition. The overall structure of the study takes the form of three major steps includes pre-processing techniques, feature extraction and matching process before proceeds with fusion to combine the holistic and local features. Pre-processing technique is the initial stage to make sure the palmprint image from dataset is cropped and resized into the specific size. The entire proposed method is validated using benchmark PolyU dataset for palmprint recognition analysis. Gabor filter pattern is used to extract important information in holistic features while Discrete Cosine Transform (DCT) is used to extract low frequency energy of local features. Then, the computation of Principal Component Analysis (PCA) is applied to reduce the high dimensional feature space to low dimensional feature space. Low dimensional feature space preserved low frequency information. Classification is the process used to distinguish and classify a new observation based on the training set of data. The purpose of Euclidean distance classifier is to measure the matching value and its closeness between the training and testing feature vectors. Among various fusion levels, matching score level fusion is the most suitable approach in combining the match score from two different matchers because this fusion rule can increase matching accuracy. It is developed by forming a single value for decision process from the matching output of different matching module. In the matching score fusion scheme, weighted sum rule produced superior performance. The best recognition rate of 97% is achieved using 100 subjects. Based on the recognition analysis, there are three important parameters that affect the performance which is the size of input image, the effect of principal components number, and the number of DCT coefficient.
format	Thesis
title	Fusion of holistic and local features for palmprint recognition
title_short	Fusion of holistic and local features for palmprint recognition
title_full	Fusion of holistic and local features for palmprint recognition
title_fullStr	Fusion of holistic and local features for palmprint recognition
title_full_unstemmed	Fusion of holistic and local features for palmprint recognition
title_sort	fusion of holistic and local features for palmprint recognition
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/76738/3/Declaration%20Form.pdf
_version_	1776104274400378880

Fusion of holistic and local features for palmprint recognition

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