Integration of blcm and flbp in low resolution face recognition

Face recognition from face image has been a fast-growing topic in biometrics research community and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. These techniques work well on grayscale and colour images with very few techniques...

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Main Author:	Mohammed, Ahmed Talab
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/39215/1/ir.Integration%20of%20blcm%20and%20flbp%20in%20low%20resolution%20face%20recognition.pdf
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spelling	my-ump-ir.392152023-11-08T03:30:02Z Integration of blcm and flbp in low resolution face recognition 2022-08 Mohammed, Ahmed Talab QA75 Electronic computers. Computer science QA76 Computer software Face recognition from face image has been a fast-growing topic in biometrics research community and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. These techniques work well on grayscale and colour images with very few techniques deal with binary and low resolution image. With binary image becoming the preferred format for low face resolution analysis, there is need for further studies to provide a complete solution for image-based face recognition system with higher accuracy. To overcome the limitation of the existing techniques in extracting distinctive features in low resolution images due to the contrast between the face and background, we proposed a statistical feature analysis technique to fill in the gaps. To achieve this, the proposed technique integrates Binary Level Occurrence Matrix (BLCM) and Fuzzy Local Binary Pattern (FLBP) named BLCM-FLBP to extract global and local features of face from face low resolution images. The purpose of BLCM-FLBP is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of face pattern. Experimental results on Yale and FEI datasets validates the superiority of the proposed technique over the other top-performing feature analysis techniques methods by utilizing different classifier which is Neural network (NN) and Random Forest (RF). The proposed technique achieved performance accuracy of 93.16% (RF), 95.27% (NN) when FEI dataset used, and the accuracy of 94.54% (RF), 93.61% (NN) when Yale.B used. Hence, the proposed technique outperforming other technique such as Gray Level Co-Occurrence Matrix (GLCM), Bag of Word (BOW), Fuzzy Local Binary Pattern (FLBP) respectively and Binary Level Occurrence Matrix (BLCM). 2022-08 Thesis http://umpir.ump.edu.my/id/eprint/39215/ http://umpir.ump.edu.my/id/eprint/39215/1/ir.Integration%20of%20blcm%20and%20flbp%20in%20low%20resolution%20face%20recognition.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing Suryanti, Awang
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Suryanti, Awang
topic	QA75 Electronic computers Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers Computer science QA76 Computer software Mohammed, Ahmed Talab Integration of blcm and flbp in low resolution face recognition
description	Face recognition from face image has been a fast-growing topic in biometrics research community and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. These techniques work well on grayscale and colour images with very few techniques deal with binary and low resolution image. With binary image becoming the preferred format for low face resolution analysis, there is need for further studies to provide a complete solution for image-based face recognition system with higher accuracy. To overcome the limitation of the existing techniques in extracting distinctive features in low resolution images due to the contrast between the face and background, we proposed a statistical feature analysis technique to fill in the gaps. To achieve this, the proposed technique integrates Binary Level Occurrence Matrix (BLCM) and Fuzzy Local Binary Pattern (FLBP) named BLCM-FLBP to extract global and local features of face from face low resolution images. The purpose of BLCM-FLBP is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of face pattern. Experimental results on Yale and FEI datasets validates the superiority of the proposed technique over the other top-performing feature analysis techniques methods by utilizing different classifier which is Neural network (NN) and Random Forest (RF). The proposed technique achieved performance accuracy of 93.16% (RF), 95.27% (NN) when FEI dataset used, and the accuracy of 94.54% (RF), 93.61% (NN) when Yale.B used. Hence, the proposed technique outperforming other technique such as Gray Level Co-Occurrence Matrix (GLCM), Bag of Word (BOW), Fuzzy Local Binary Pattern (FLBP) respectively and Binary Level Occurrence Matrix (BLCM).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammed, Ahmed Talab
author_facet	Mohammed, Ahmed Talab
author_sort	Mohammed, Ahmed Talab
title	Integration of blcm and flbp in low resolution face recognition
title_short	Integration of blcm and flbp in low resolution face recognition
title_full	Integration of blcm and flbp in low resolution face recognition
title_fullStr	Integration of blcm and flbp in low resolution face recognition
title_full_unstemmed	Integration of blcm and flbp in low resolution face recognition
title_sort	integration of blcm and flbp in low resolution face recognition
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/39215/1/ir.Integration%20of%20blcm%20and%20flbp%20in%20low%20resolution%20face%20recognition.pdf
_version_	1783732299966185472

Integration of blcm and flbp in low resolution face recognition

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