Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm

Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can app...

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Autores principales:	Wang Dongshu, Wang Heshan, Sun Jiwen, Xin Jianbin, Luo Yong
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	face recognition where-what networks synapse maintenance mechanism neuron regenesis mechanism Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/db38578476d7433e8317d5d89a87c887
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id	oai:doaj.org-article:db38578476d7433e8317d5d89a87c887
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spelling	oai:doaj.org-article:db38578476d7433e8317d5d89a87c8872021-12-05T14:10:51ZFace Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm2191-026X10.1515/jisys-2019-0114https://doaj.org/article/db38578476d7433e8317d5d89a87c8872020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0114https://doaj.org/toc/2191-026XFace recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the recognition performance, synapse maintenance mechanism and neuron regenesis mechanism are both introduced. Synapse maintenance is used to reduce the background interference while neuron regenesis mechanism is introduced to regulate the neuron resource dynamically to improve the network usage efficiency. Experiments have been conducted on human face images of 5 types, 11 sizes, and 225 locations in complex backgrounds. Experiment results demonstrate that the proposed WWN model can basically learn three concepts (type, location and size) simultaneously. The experiment results also show the advantages of the enhanced WWN-7 model for face recognition in comparison with several existing methods.Wang DongshuWang HeshanSun JiwenXin JianbinLuo YongDe Gruyterarticleface recognitionwhere-what networkssynapse maintenance mechanismneuron regenesis mechanismScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 18-39 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	face recognition where-what networks synapse maintenance mechanism neuron regenesis mechanism Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	face recognition where-what networks synapse maintenance mechanism neuron regenesis mechanism Science Q Electronic computers. Computer science QA75.5-76.95 Wang Dongshu Wang Heshan Sun Jiwen Xin Jianbin Luo Yong Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
description	Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the recognition performance, synapse maintenance mechanism and neuron regenesis mechanism are both introduced. Synapse maintenance is used to reduce the background interference while neuron regenesis mechanism is introduced to regulate the neuron resource dynamically to improve the network usage efficiency. Experiments have been conducted on human face images of 5 types, 11 sizes, and 225 locations in complex backgrounds. Experiment results demonstrate that the proposed WWN model can basically learn three concepts (type, location and size) simultaneously. The experiment results also show the advantages of the enhanced WWN-7 model for face recognition in comparison with several existing methods.
format	article
author	Wang Dongshu Wang Heshan Sun Jiwen Xin Jianbin Luo Yong
author_facet	Wang Dongshu Wang Heshan Sun Jiwen Xin Jianbin Luo Yong
author_sort	Wang Dongshu
title	Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
title_short	Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
title_full	Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
title_fullStr	Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
title_full_unstemmed	Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm
title_sort	face recognition in complex unconstrained environment with an enhanced wwn algorithm
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/db38578476d7433e8317d5d89a87c887
work_keys_str_mv	AT wangdongshu facerecognitionincomplexunconstrainedenvironmentwithanenhancedwwnalgorithm AT wangheshan facerecognitionincomplexunconstrainedenvironmentwithanenhancedwwnalgorithm AT sunjiwen facerecognitionincomplexunconstrainedenvironmentwithanenhancedwwnalgorithm AT xinjianbin facerecognitionincomplexunconstrainedenvironmentwithanenhancedwwnalgorithm AT luoyong facerecognitionincomplexunconstrainedenvironmentwithanenhancedwwnalgorithm
_version_	1718371693179699200

Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm

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