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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De Gruyter
2021
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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) |
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gait recognition subspace learning distance metric learning Science Q Electronic computers. Computer science QA75.5-76.95 |
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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 |