Deep Large Margin Nearest Neighbor for Gait Recognition

Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolu...

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Main Author:	Xu Wanjiang
Format:	article
Language:	EN
Published:	De Gruyter 2021
Subjects:	gait recognition subspace learning distance metric learning Science Q Electronic computers. Computer science QA75.5-76.95
Online Access:	https://doaj.org/article/4f04334e1af34b11be30d1da451dfccc
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id	oai:doaj.org-article:4f04334e1af34b11be30d1da451dfccc
record_format	dspace
spelling	oai:doaj.org-article:4f04334e1af34b11be30d1da451dfccc2021-12-05T14:10:51ZDeep Large Margin Nearest Neighbor for Gait Recognition0334-18602191-026X10.1515/jisys-2020-0077https://doaj.org/article/4f04334e1af34b11be30d1da451dfccc2021-05-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0077https://doaj.org/toc/0334-1860https://doaj.org/toc/2191-026XGait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.Xu WanjiangDe Gruyterarticlegait recognitionsubspace learningdistance metric learningScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 604-619 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gait recognition subspace learning distance metric learning Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	gait recognition subspace learning distance metric learning Science Q Electronic computers. Computer science QA75.5-76.95 Xu Wanjiang Deep Large Margin Nearest Neighbor for Gait Recognition
description	Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.
format	article
author	Xu Wanjiang
author_facet	Xu Wanjiang
author_sort	Xu Wanjiang
title	Deep Large Margin Nearest Neighbor for Gait Recognition
title_short	Deep Large Margin Nearest Neighbor for Gait Recognition
title_full	Deep Large Margin Nearest Neighbor for Gait Recognition
title_fullStr	Deep Large Margin Nearest Neighbor for Gait Recognition
title_full_unstemmed	Deep Large Margin Nearest Neighbor for Gait Recognition
title_sort	deep large margin nearest neighbor for gait recognition
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/4f04334e1af34b11be30d1da451dfccc
work_keys_str_mv	AT xuwanjiang deeplargemarginnearestneighborforgaitrecognition
_version_	1718371665843322880

Deep Large Margin Nearest Neighbor for Gait Recognition

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