Residual connection-based graph convolutional neural networks for gait recognition-Reference-Cited by-同舟云学术

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Residual connection-based graph convolutional neural networks for gait recognition

Published:2021-07-16 Issue:9-11 Volume:37 Page:2713-2724
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Shopon Md^ORCID,Bari A. S. M. Hossain,Gavrilova Marina L.

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-021-02245-9.pdf

Reference52 articles.

1. Xiao, Q.: Technology review-biometrics-technology, application, challenge, and computational intelligence solutions. IEEE Comput.l Intell. Mag. 2(2), 5–25 (2007)

2. Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Sig. Process. Mag. 22(6), 78–90 (2005)

3. Ahmed, F., Paul, P.P., Gavrilova, M.L.: DTW-based kernel and rank-level fusion for 3d gait recognition using kinect. The Vis. Comput. 31(6), 915–924 (2015)

4. Obaidat, M.S., Traore, I., Woungang, I.: Biometric-Based Physical and Cybersecurity Systems. Springer, Berlin (2019)

5. Zhang, Y., Zheng, J., Magnenat-Thalmann, N.: Example-guided anthropometric human body modeling. The Vis. Comput. 31(12), 1615–1631 (2015)

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