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Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory
Open accessAbstract
Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person’s gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.
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“Concerning Recurrent Neural Networks [43], Wang and Yan [115] used the Long short-term memory [32], a popular RNN architecture capable of learning long-term dependencies, for crossview human gait recognition based on frame-by-frame GEIs. Experiments conducted over CASIA B [128] and OU-ISIR [59] Large Population datasets demonstrate the model's robustness over several baselines.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [115]
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Individual recognition using gait energy image
Han, J., & Bhanu, B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.38 · Cited by 1,873
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1096 · Cited by 1,140
The humanID gait challenge problem: data sets, performance, and analysis
Sarkar, S., Phillips, P. J., Liu, Z., Vega, I. R., Grother, P., & Bowyer, K. W. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.39 · Cited by 1,133
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
Wu, Z., Huang, Y., Wang, L., Wang, X., & Tan, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2545669 · Cited by 741
Self-citationLi, S., Zhao, X., & Zhou, G. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12433 · Cited by 500
GEINet: View-invariant gait recognition using a convolutional neural network
Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., & Yagi, Y. (2016) · 2016 International Conference on Biometrics (ICB) · doi:10.1109/icb.2016.7550060 · Cited by 468
Encoder–decoder network for pixel-level road crack detection in black-box images
Bang, S., Park, S., Kim, H., & Kim, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12440 · Cited by 429
Liang, X. P. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12425 · Cited by 406
Research works printed but not linked: 5
- Torres J., 2018, Integr. Comput Aided Eng., 25, 49
- Zhao G., 2006, Int. Conf. Automatic Face and Gesture Recognition, 529
- Jia N., 2017, Int. Joint Conf. Biometrics, 774
- Takemura N., 2018, IEEE Trans. Circ. Syst. Video Technol., 28
- Ko B., 2017, IEEE Int. Conf. Big Data and Smart Computing (BigComp), 358
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Metadata
- Authors
- Wang, X., & Yan, W. Q.
- Affiliations
- China Jiliang University (CN)
- Auckland University of Technology (NZ)
- Venue
- International Journal of Neural Systems
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2019-11-21
- Volume
- 30
- Issue
- 01
- Pages
- 1950027
- ISSN
- 0129-0657 (ISSN-L)
- 1793-6462
- OpenAlex
- W2973839564
- Cited by
- 116
- References
- 45
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 16 · 2023: 25 · 2022: 18 · 2021: 17 · 2020: 10 · 2019: 1
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- International Journal of Neural Systems (publishedVersion)
- PubMed (publishedVersion)
- Tuwhera (Auckland University of Technology) (submittedVersion, other-oa)
- Funders
- the National Natural Science Foundation of China (61303146, 61602431)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- gait recognition, gait energy image, convolutional LSTM, cross-view gait recognition, gait cycle segmentation, biometric identification, video surveillance, CASIA-B
- MeSH terms
- Deep Learning, Adult, Humans, Memory, Short-Term, Models, Biological, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Neural Networks, Computer, Memory, Long-Term, Datasets as Topic
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