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Learning optimised representations for view-invariant gait recognition
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Jia, Ning, Sanchez Silva, Victor and Li, Chang-Tsun (2018) Learning optimised representations for view-invariant gait recognition. In: IAPR/IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 1-4 Oct 2017 ISBN 9781538611241. doi:10.1109/BTAS.2017.8272769
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WRAP-learning-optimised-representations-view-invariant-gait-recognition-Li-2018.pdf - Accepted Version - Requires a PDF viewer. Download (869Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/BTAS.2017.8272769
Abstract
Gait recognition can be performed without subject cooperation under harsh conditions, thus it is an important tool in forensic gait analysis, security control, and other commercial applications. One critical issue that prevents gait recognition systems from being widely accepted is the performance drop when the camera viewpoint varies between the registered templates and the query data. In this paper, we explore the potential of combining feature optimisers and representations learned by convolutional neural networks (CNN) to achieve efficient view-invariant gait recognition. The experimental results indicate that CNN learns highly discriminative representations across moderate view variations, and these representations can be further improved using view-invariant feature selectors, achieving a high matching accuracy across views.
Item Type: | Conference Item (Paper) | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Gait in humans, Gait in humans -- Computer programs, Gait in humans -- Computer simulation, Biometric identification, Pattern recognition systems, Computer vision | ||||||||
Publisher: | IEEE | ||||||||
ISBN: | 9781538611241 | ||||||||
Official Date: | 2018 | ||||||||
Dates: |
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DOI: | 10.1109/BTAS.2017.8272769 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 19 May 2020 | ||||||||
Date of first compliant Open Access: | 19 May 2020 | ||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||
Title of Event: | IAPR/IEEE International Joint Conference on Biometrics (IJCB) | ||||||||
Type of Event: | Conference | ||||||||
Location of Event: | Denver, CO, USA | ||||||||
Date(s) of Event: | 1-4 Oct 2017 |
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