Abstract
Gait recognition is a biometric technology based on the human walking state. Unlike other biometric technologies, gait recognition can be used for remote recognition and the human walking pattern cannot be imitated. Gait recognition has wide applications in the field of criminal investigation, security and other fields. Most of the current mainstream algorithms use Convolutional Neural Network (CNN) to extract gait features. However, CNN only captures the local image features in most cases which may not inherently capture global context or long-range dependencies. In order to solve the above problems and to extract more comprehensive and precise feature representations, we propose a novel Gait recognition algorithm jointing Transformer and CNN by introducing the attention mechanism, called GaitTC. The framework consists of three modules, including the Transformer module, CNN module and feature aggregation module. In this paper, we conduct the experiments on CASIA-B dataset. The results of the experiments show that the proposed gait recognition method achieves relatively good performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hu, M., Wang, Y., Zhang, Z., Little, J., et al.: View-invariant discriminative projection for multi-view gait-based human identification, pp. 2034–2045 (2013)
Kusakunniran, W., Wu, Q., Zhang, J., et al.: Recognizing gaits across views through correlated motion co-clustering. IEEE Trans. Image Process. 23(2), 696–709 (2014)
Wu, Z., Huang, Y., Wang, L., et al.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39, 209–226 (2016)
Chao, H., Wang, K., He, Y., et al.: GaitSet: cross-view gait recognition through utilizing gait as a deep set. Cornell University – arXiv:2102.03247v1 (2021)
Rida, I., Almaadeed, N., Almaadeed, S.: Robust gait recognition: a comprehensive survey. IET Biomet. 8, 14–28 (2018)
Zhao, G., Liu, G., Li, H., et al.: 3D gait recognition using multiple cameras, pp. 529–534 (2006)
Zheng, X., Li, X., Xu, K., et al.: Gait identification under surveillance environment based on human skeleton. arXiv preprint arXiv:2111.11720 (2021)
Shiraga, K., Makihara, Y., Muramatsu, D., et al.: GEINet: view-invariant gait recognition using a convolutional neural network. In: 2016 International Conference on Biometrics (ICB), Halmstad, Sweden, pp. 1–8 (2016)
Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474–483. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_51
Huang, Z., Xue, D., Shen, X., et al.: 3D local convolutional neural networks for gait recognition. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada (2022)
Wolf, T., Babaee, M., Rigoll, G.: Multi-view gait recognition using 3D convolutional neural networks. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, pp. 14920–14929 (2016)
He, Y., Zhang, J., Shan, H., et al.: Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans. Inf. Forensics Secur. 102–113 (2018)
Yu S, Wang, Q., Shen, L., et al.: View invariant gait recognition using only one uniform model. In: 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, vol. 239, pp. 81–93 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cai, M., Wang, M., Zhang, S. (2023). Gait Recognition by Jointing Transformer and CNN. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-8565-4_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8564-7
Online ISBN: 978-981-99-8565-4
eBook Packages: Computer ScienceComputer Science (R0)