Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition

Face recognition under different illumination remains a challenging problem. The variations between the images of the same face due to illuminations are almost always being larger than image variations due to changes in face identity. For finger vein recognition, the recognition rate may be de...

Full description

Saved in:

Bibliographic Details
Main Author:	Chaiwuh, Shing
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/45063/1/Chaiwuh%20Shing24.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.45063
record_format	uketd_dc
spelling	my-usm-ep.450632019-07-25T08:08:04Z Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition 2013-07 Chaiwuh, Shing TK1-9971 Electrical engineering. Electronics. Nuclear engineering Face recognition under different illumination remains a challenging problem. The variations between the images of the same face due to illuminations are almost always being larger than image variations due to changes in face identity. For finger vein recognition, the recognition rate may be degraded due to low quality of finger vein images. This is because finger vein images are not always clear and can display irregular shadings. A theoretically simple, yet efficient technique, called Improved Local Line Binary Pattern (ILLBP) has been proposed in order to solve the problems. The descriptor can be used for both face and finger vein recognition. The effectiveness of the proposed technique is empirically demonstrated using Principal Component Analysis-k-Nearest Neighbor (PCA-kNN), Multiclass Support Vector Machine (Multiclass SVM) and Hamming Distance(HD) as the classifiers. Comparisons among other existing Local Binary Pattern (LBP) variants on the Yale Face Database B, Extended Yale Face Database B and our own finger vein database have been conducted. The advantages of our technique include higher accuracy compared to other LBP variants and fast computational time. The experimental results for face recognition showed that by using PCA-kNN, the best ILLBP (N = 15, P = 2) achieved a high recognition rate (89.24%) only slightly worse than the best LLBP with N = 17 (89.36%). 2013-07 Thesis http://eprints.usm.my/45063/ http://eprints.usm.my/45063/1/Chaiwuh%20Shing24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Chaiwuh, Shing Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
description	Face recognition under different illumination remains a challenging problem. The variations between the images of the same face due to illuminations are almost always being larger than image variations due to changes in face identity. For finger vein recognition, the recognition rate may be degraded due to low quality of finger vein images. This is because finger vein images are not always clear and can display irregular shadings. A theoretically simple, yet efficient technique, called Improved Local Line Binary Pattern (ILLBP) has been proposed in order to solve the problems. The descriptor can be used for both face and finger vein recognition. The effectiveness of the proposed technique is empirically demonstrated using Principal Component Analysis-k-Nearest Neighbor (PCA-kNN), Multiclass Support Vector Machine (Multiclass SVM) and Hamming Distance(HD) as the classifiers. Comparisons among other existing Local Binary Pattern (LBP) variants on the Yale Face Database B, Extended Yale Face Database B and our own finger vein database have been conducted. The advantages of our technique include higher accuracy compared to other LBP variants and fast computational time. The experimental results for face recognition showed that by using PCA-kNN, the best ILLBP (N = 15, P = 2) achieved a high recognition rate (89.24%) only slightly worse than the best LLBP with N = 17 (89.36%).
format	Thesis
qualification_level	Master's degree
author	Chaiwuh, Shing
author_facet	Chaiwuh, Shing
author_sort	Chaiwuh, Shing
title	Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
title_short	Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
title_full	Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
title_fullStr	Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
title_full_unstemmed	Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition
title_sort	improved local line binary pattern (illbp): an improved lbp-based biometric descriptor for face and finger vein recognition
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2013
url	http://eprints.usm.my/45063/1/Chaiwuh%20Shing24.pdf
_version_	1747821448256290816

Improved Local Line Binary Pattern (Illbp): An Improved Lbp-Based Biometric Descriptor For Face And Finger Vein Recognition

Similar Items