Gait recognition using a few gait frames
Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-382.pdf |
| _version_ | 1818444105642934272 |
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| author | Lingxiang Yao Worapan Kusakunniran Qiang Wu Jian Zhang |
| author_facet | Lingxiang Yao Worapan Kusakunniran Qiang Wu Jian Zhang |
| author_sort | Lingxiang Yao |
| collection | DOAJ |
| description | Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition. |
| first_indexed | 2024-12-14T19:10:39Z |
| format | Article |
| id | doaj.art-f558453ccce34663bab22aced646d52c |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-14T19:10:39Z |
| publishDate | 2021-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-f558453ccce34663bab22aced646d52c2022-12-21T22:50:44ZengPeerJ Inc.PeerJ Computer Science2376-59922021-03-017e38210.7717/peerj-cs.382Gait recognition using a few gait framesLingxiang Yao0Worapan Kusakunniran1Qiang Wu2Jian Zhang3School of Electrical and Data Engineering, University of Technology Sydney, Sydney, AustraliaFaculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, ThailandSchool of Electrical and Data Engineering, University of Technology Sydney, Sydney, AustraliaSchool of Electrical and Data Engineering, University of Technology Sydney, Sydney, AustraliaGait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.https://peerj.com/articles/cs-382.pdfGait recognitionLimited gait framesSilhouetteSkeletonTwo-branch network |
| spellingShingle | Lingxiang Yao Worapan Kusakunniran Qiang Wu Jian Zhang Gait recognition using a few gait frames PeerJ Computer Science Gait recognition Limited gait frames Silhouette Skeleton Two-branch network |
| title | Gait recognition using a few gait frames |
| title_full | Gait recognition using a few gait frames |
| title_fullStr | Gait recognition using a few gait frames |
| title_full_unstemmed | Gait recognition using a few gait frames |
| title_short | Gait recognition using a few gait frames |
| title_sort | gait recognition using a few gait frames |
| topic | Gait recognition Limited gait frames Silhouette Skeleton Two-branch network |
| url | https://peerj.com/articles/cs-382.pdf |
| work_keys_str_mv | AT lingxiangyao gaitrecognitionusingafewgaitframes AT worapankusakunniran gaitrecognitionusingafewgaitframes AT qiangwu gaitrecognitionusingafewgaitframes AT jianzhang gaitrecognitionusingafewgaitframes |