A Robust Illumination and Intensity invariant Face Recognition System

Meena, MS; Pare, S; Singh, P; Rana, A; Prasad, M

A Robust Illumination and Intensity invariant Face Recognition System

Meena, MS Pare, S Singh, P Rana, A Prasad, M

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Publisher:: North Atlantic University Union (NAUN)
Publication Type:: Journal Article
Citation:: International Journal of Circuits, Systems and Signal Processing, 2022, 16, pp. 974-984
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Meena, MS
dc.contributor.author	Pare, S
dc.contributor.author	Singh, P
dc.contributor.author	Rana, A
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-19T22:43:53Z
dc.date.available	2023-03-19T22:43:53Z
dc.date.issued	2022-01-01
dc.identifier.citation	International Journal of Circuits, Systems and Signal Processing, 2022, 16, pp. 974-984
dc.identifier.issn	1998-4464
dc.identifier.issn	1998-4464
dc.identifier.uri	http://hdl.handle.net/10453/167599
dc.description.abstract	Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively.
dc.language	en
dc.publisher	North Atlantic University Union (NAUN)
dc.relation.ispartof	International Journal of Circuits, Systems and Signal Processing
dc.relation.isbasedon	10.46300/9106.2022.16.119
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Robust Illumination and Intensity invariant Face Recognition System
dc.type	Journal Article
utslib.citation.volume	16
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-19T22:43:52Z
pubs.publication-status	Published
pubs.volume	16

Abstract:

Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively.

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http://hdl.handle.net/10453/167599