Research Article
Walking Pattern Recognition using Generative Adversarial Network
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 184 - Issue 40 |
| Published: Dec 2022 |
| Authors: Md. Abu Bakar Siddique Sadi, Turshin Ara Ashtary, Banna Sreya Sarker, Sifat Rahman Ahona |
10.5120/ijca2022922510
|
Md. Abu Bakar Siddique Sadi, Turshin Ara Ashtary, Banna Sreya Sarker, Sifat Rahman Ahona . Walking Pattern Recognition using Generative Adversarial Network. International Journal of Computer Applications. 184, 40 (Dec 2022), 32-36. DOI=10.5120/ijca2022922510
@article{ 10.5120/ijca2022922510,
author = { Md. Abu Bakar Siddique Sadi,Turshin Ara Ashtary,Banna Sreya Sarker,Sifat Rahman Ahona },
title = { Walking Pattern Recognition using Generative Adversarial Network },
journal = { International Journal of Computer Applications },
year = { 2022 },
volume = { 184 },
number = { 40 },
pages = { 32-36 },
doi = { 10.5120/ijca2022922510 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2022
%A Md. Abu Bakar Siddique Sadi
%A Turshin Ara Ashtary
%A Banna Sreya Sarker
%A Sifat Rahman Ahona
%T Walking Pattern Recognition using Generative Adversarial Network%T
%J International Journal of Computer Applications
%V 184
%N 40
%P 32-36
%R 10.5120/ijca2022922510
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Walking pattern recognition is a fascinating biometric modality that seeks to identify people based on how they walk. Its advantage over other biometrics is that it doesn't need subjects to cooperate. By recognizing people based on how they walk, it essentially seeks to alleviate this issue.
References
- Wang, W., Zhang, L., Liu, J., Zhang, B., & Huang, Q. (2020). A real-time walking pattern recognition method for soft knee power assist wear. International Journal of Advanced Robotic Systems, 17(3), 1729881420925291.
- Khan, A., Khan, M. A., Javed, M. Y., Alhaisoni, M., Tariq, U., Kadry, S., ... & Nam, Y. (2022). Human gait recognition using deep learning and improved ant colony optimization.
- Davis, J. W., & Taylor, S. R. (2002, August). Analysis and recognition of walking movements. In 2002 International Conference on Pattern Recognition (Vol. 1, pp. 315-318). IEEE.
- Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585-595.
- Saha, S., Ghosh, S., Konar, A., & Janarthanan, R. (2013, August). A study on leg posture recognition from Indian classical dance using Kinect sensor. In 2013 International Conference on Human Computer Interactions (ICHCI) (pp. 1-6). IEEE.
- Tanawongsuwan, R., & Bobick, A. (2001, December). Gait recognition from time-normalized joint-angle trajectories in the walking plane. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 2, pp. II-II). IEEE.
- Ameli, S., Naghdy, F., Stirling, D., Naghdy, G., & Aghmesheh, M. (2017). Objective clinical gait analysis using inertial sensors and six minute walking test. Pattern Recognition, 63, 246-257.Wang, L., Tan, T., Hu, W., & Ning, H. (2003). Automatic gait recognition based on statistical shape analysis. IEEE transactions on image processing, 12(9), 1120-1131.
- Makihara, Y., Nixon, M. S., & Yagi, Y. (2020). Gait recognition: Databases, representations, and applications. Computer Vision: A Reference Guide, 1-13.
- Amin, T. (2013). Dynamic descriptors in human gait recognition. University of Toronto (Canada).
Index Terms
Keywords