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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Wang and Yan used LSTM for cross-view gait recognition based on frame-by-frame GEIs, and experiments on CASIA B and OU-ISIR demonstrate robustness over baselines.
“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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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 731 · see citing works
Self-citationLi, S., Zhao, X., & Zhou, G. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12433 · Cited by 495 · see citing works
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 · see citing works
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 421 · see citing works
Rafiei, M. H., & Adeli, H. (2017) · Engineering Structures · doi:10.1016/j.engstruct.2017.10.070 · Cited by 404 · see citing works
Liang, X. P. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12425 · Cited by 401 · see citing works
Iwama, H., Okumura, M., Makihara, Y., & Yagi, Y. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2204253 · Cited by 394 · see citing works
Gait Recognition Using a View Transformation Model in the Frequency Domain
Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., & Yagi, Y. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744078_12 · Cited by 392 · see citing works
A novel machine learning‐based algorithm to detect damage in high‐rise building structures
Rafiei, M. H., & Adeli, H. (2017) · The Structural Design of Tall and Special Buildings · doi:10.1002/tal.1400 · Cited by 348 · see citing works
Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete
Rafiei, M. H., Khushefati, W. H., Demirboğa, R., & Adeli, H. (2017) · ACI Materials Journal · doi:10.14359/51689560 · Cited by 277 · see citing works
Rafiei, M. H., & Adeli, H. (2018) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001570 · Cited by 272 · see citing works
Multi-view gait recognition using 3D convolutional neural networks
Wolf, T., Babaee, M., & Rigoll, G. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533144 · Cited by 241 · see citing works
Self-Calibrating View-Invariant Gait Biometrics
Goffredo, M., Bouchrika, I., Carter, J. N., & Nixon, M. S. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2031091 · Cited by 204 · see citing works
Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image
Kusakunniran, W., Wu, Q., Li, H., & Zhang, J. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457587 · Cited by 195 · see citing works
The Relation between the ROC Curve and the CMC
Bolle, R. M., Connell, J. H., Pankanti, S., Ratha, N., & Senior, A. (2006) · IEEE Workshop on Automatic Identification Advanced Technologies (AutoID) · doi:10.1109/autoid.2005.48 · Cited by 187 · see citing works
Model-based 3D gait biometrics
Ariyanto, G., & Nixon, M. S. (2011) · doi:10.1109/ijcb.2011.6117582 · Cited by 129 · see citing works
On Reducing the Effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method
Guan, Y., Li, C.-T., & Roli, F. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366766 · Cited by 128 · see citing works
A scalable approach based on deep learning for big data time series forecasting
Torres, J. F., Galicia, A., Troncoso, A., & Alvarez, F. M. (2018) · Integrated Computer-Aided Engineering · doi:10.3233/ica-180580 · Cited by 125 · see citing works
Recognizing Gaits Across Views Through Correlated Motion Co-Clustering
Kusakunniran, W., Wu, Q., Zhang, J., Li, H., & Wang, L. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2294552 · Cited by 119 · see citing works
Luo, X., Li, H., Yang, X., Yu, Y., & Cao, D. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12419 · Cited by 112 · see citing works
Preconditioned Stochastic Gradient Descent
Li, X.-L. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2672978 · Cited by 109 · see citing works
Gait recognition based on dynamic region analysis
Yang, X., Zhou, Y., Zhang, T., Shu, G., & Yang, J. (2008) · Signal Processing · doi:10.1016/j.sigpro.2008.03.006 · Cited by 107 · see citing works
A New View-Invariant Feature for Cross-View Gait Recognition
Kusakunniran, W., Wu, Q., Zhang, J., Ma, Y., & Li, H. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2252342 · Cited by 98 · see citing works
Wu, R., Singla, A., Jahanshahi, M. R., Bertino, E., Ko, B. J., & Verma, D. C. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12449 · Cited by 93 · see citing works
Global Convergence of Online BP Training With Dynamic Learning Rate
Zhang, R., Xu, Z., Huang, G.-B., & Wang, D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2011.2178315 · Cited by 79 · see citing works
Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring
Huang, Y., Beck, J. L., & Li, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12408 · Cited by 79 · see citing works
Robust Arbitrary-View Gait Recognition Based on 3D Partial Similarity Matching
Tang, J., Luo, J., Tjahjadi, T., & Guo, F. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2612823 · Cited by 71 · see citing works
Gait Recognition With Shifted Energy Image and Structural Feature Extraction
Huang, X., & Boulgouris, N. V. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2180914 · Cited by 67 · see citing works
Regional parallel structure based CNN for thermal infrared face identification
Wang, P., & Bai, X. (2018) · Integrated Computer-Aided Engineering · doi:10.3233/ica-180560 · Cited by 66 · see citing works
Molina‐Cabello, M. A., Luque‐Baena, R. M., López‐Rubio, E., & Thurnhofer‐Hemsi, K. (2018) · Integrated Computer-Aided Engineering · doi:10.3233/ica-180577 · Cited by 62 · see citing works
End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management
Hashemi, H., & Abdelghany, K. F. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12376 · Cited by 59 · see citing works
Gait recognition based on Gabor wavelets and (2D)2PCA
Wang, X., Wang, J., & Yan, K. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4903-7 · Cited by 55 · see citing works
Self-citationCovariate Conscious Approach for Gait Recognition Based Upon Zernike Moment Invariants
Aggarwal, H., & Vishwakarma, D. K. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2658674 · Cited by 51 · see citing works
Luo, J., Tang, J., Tjahjadi, T., & Xiao, X. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.030 · Cited by 48 · see citing works
Maeda, K., Takahashi, S., Ogawa, T., & Haseyama, M. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12451 · Cited by 40 · see citing works
A Grassmannian Approach to Address View Change Problem in Gait Recognition
Connie, T., Goh, M. K. O., & Teoh, A. B. J. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2545693 · Cited by 39 · see citing works
Gait Recognition Using HMMs and Dual Discriminative Observations for Sub-Dynamics Analysis
Boulgouris, N. V., & Huang, X. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2266578 · Cited by 33 · see citing works
Schetinin, V., Jakaite, L., & Krzanowski, W. J. (2018) · Integrated Computer-Aided Engineering · doi:10.3233/ica-180567 · Cited by 20 · see citing works
Gait recognition using GEI and pattern trace transform
Theekhanont, P., Kurutach, W., & Miguet, S. (2012) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itime.2012.6291457 · Cited by 11 · see citing works
Performance analysis of gait recognition with large perspective distortion
Abdulsattar, F. S., & Carter, J. (2016) · IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) · doi:10.1109/isba.2016.7477229 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- M. Rafiei and H. Adeli, Novel machine learning model for construction cost estimation taking into account economic variables and indices, Journal of Construction Engineering and Management 144(12) (2018) 1-9.
- G. Zhao, G. Liu, H. Li and M. Pietikainen, 3D gait recognition using multiple cameras, Interna- tional Conference on Automatic Face and Gesture Recognition, (Southampton, UK, 2006).
- N. Jia, V. Sanchez and C. Li, Learning optimized representations for view-invariant gait recognition, International Joint Conference on Biometrics, (Den- ver, USA, 2017), pp. 774-780.
- N. Takemura, Y. Makihara, D. Muramatsu, T. Echigo and Y. Yagi, On input/output architec- tures for convolutional neural network-based cross- view gait recognition, IEEE Transactions on Cir- cuits and Systems for Video Technology 28(1) (2018).
- B. Ko, H. Kim, K. Oh and H. Choi, Controlled dropout: A different approach to using dropout on deep neural network, IEEE International Confer- ence on Big Data and Smart Computing (BigComp), (Jeju, South Korea, 2017), pp. 358-362.
- S. Yu, D. Tan and T. Tan, A framework for evalu- ating the effect of view angle, clothing and carrying condition on gait recognition, International Confer- ence on Pattern Recognition, (Hong Kong, China, 2006), pp. 441-444.
- I. Good- fellow, Y. Bengio and A. Courville, Deep Learning (MIT Press, 2016). http://www.deeplearningbook.org .
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Self-citationSalcedo, E. (2024) · Journal of Imaging · doi:10.3390/jimaging10120326 · Cited by 10 · see citing works
Gait recognition methods in the task of biometric human identification
Purish, S. V., & Lobachev, M. V. (2023) · Herald of Advanced Information Technology · doi:10.15276/hait.06.2023.1 · Cited by 8 · see citing works
A bidirectional Siamese recurrent neural network for accurate gait recognition using body landmarks
Progga, P. H., Rahman, M. J., Biswas, S., Ahmed, S., Anwary, A. R., & Shatabda, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128313 · Cited by 9 · see citing works
A method for balancing a multi-labeled biomedical dataset
Mukhin, A., Kilbas, I., Александрович, П. Р., Ilyasova, N. Y., & Kupriyanov, A. V. (2022) · Integrated Computer-Aided Engineering · doi:10.3233/ica-220676 · Cited by 8 · see citing works
Gait recognition based on sparse linear subspace
Wen, J., & Wang, X. (2021) · IET Image Processing · doi:10.1049/ipr2.12260 · Cited by 7 · see citing works
Self-citationHuman identification based on Gait Manifold
Wang, X., & Yan, W. Q. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03818-4 · Cited by 7 · see citing works
Self-citationResearch Progress on Tunable Absorbers for Various Wavelengths Based on Metasurfaces
Jiang, K., Feng, H., Gu, M., Jing, X., & Li, C. (2025) · Photonics · doi:10.3390/photonics12100968 · Cited by 6 · see citing works
Key-Frame-Aware Hierarchical Learning for Robust Gait Recognition
Wang, K., & Huo, H. (2025) · Journal of Imaging · doi:10.3390/jimaging11110402 · Cited by 5 · see citing works
Gait-Enhance:Robust Gait Recognition of Complex Walking Patterns Based on WiFi CSI
Yang, J., Liu, Y., Wu, Y. F., Yang, P., & Liu, Z. (2023) · IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10449176 · Cited by 5 · see citing works
Abnormal Behavior Recognition Based on 3D Dense Connections
Chen, W. R., Yu, Z., Yang, C., & Lu, Y. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500497 · Cited by 5 · see citing works
A Review of Deep Learning Approaches for Human Gait Recognition
Pundir, A., & Sharma, M. (2023) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon57975.2023.10101267 · Cited by 5 · see citing works
Gait identification based on deepwalk features using CNN and LSTM: an advanced biometric approach
Tiwari, R. S., Das, T. K., Tripathy, A. K., & Li, K. (2025) · Telecommunication Systems · doi:10.1007/s11235-025-01319-6 · Cited by 4 · see citing works
Convolutional Neural Networks and Recurrent Neural Networks
Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_3 · Cited by 4 · see citing works
Self-citationAn Example of Intelligent Security System Based on Deep Learning
Ramakić, A., Bundalo, Z., & Bundalo, D. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624502086 · Cited by 4 · see citing works
DeepNet-Gait: Human Identification by Gait Using Convolutional Neural Network Model
Rani, V., & Kumar, M. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10117067 · Cited by 3 · see citing works
Xu, Y., Xi, H., Ren, K., Zhu, Q., & Hu, C. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.1.013002 · Cited by 3 · see citing works
Ahmed, D. M., & Mahmood, B. S. (2023) · Traitement du signal · doi:10.18280/ts.400535 · Cited by 3 · see citing works
Musalhi, N. A., & Çelebi, E. (2024) · Traitement du signal · doi:10.18280/ts.410228 · Cited by 3 · see citing works
A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication
Yousef, R. N., Ata, M. M., Rashed, A. E. E., Badawy, M., Elhosseini, M. A., & Bahgat, W. M. (2024) · Biomimetics · doi:10.3390/biomimetics9060364 · Cited by 2 · see citing works
Hamid, J. A., N, R. P. K., Kaur, A., Chohan, M. K., Dasi, S., Kumar, P., Singh, D., & Alkhayyat, A. H. (2025) · National Academy Science Letters · doi:10.1007/s40009-025-01631-4 · Cited by 2 · see citing works
Advances in Deep Learning-Driven Metasurface Design and Application in Holographic Imaging
Lv, M., Feng, H., Jin, Y., & Tian, Y. (2025) · Photonics · doi:10.3390/photonics12100947 · Cited by 2 · see citing works
Generation and Manipulation of Perfect Vortex Beams via Metasurfaces
Feng, K., Feng, H., Gu, M., Li, C., & Jing, X. M. (2025) · Annalen der Physik · doi:10.1002/andp.202500426 · Cited by 2 · see citing works
Song, X., Qian, K., & Tang, S. (2026) · Bioengineering · doi:10.3390/bioengineering13030272 · Cited by 2 · see citing works
Systematic Literature Review: Human Gait Cycle Model Using Image-Temporal Feature
Jamal, I. H., Izhar, C. A. A., Fauzilan, A. S., Maruzuki, M. I. F., Hussain, Z., & Rahman, M. F. A. (2021) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie52900.2021.9703989 · Cited by 2 · see citing works
A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic
Sayeed, M. S., Yusof, I., Abdullah, M. F. A., Bari, M. A., & Min, P. P. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v30.i2.pp882-902 · Cited by 2 · see citing works
GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network
Khan, M. A., Khan, A. A., Alhaisoni, M. M., Alqahtani, A., Armghan, A., Althubiti, S. A., Alenezi, F. S., Mey, S., & Nam, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033856 · Cited by 2 · see citing works
Pedestrian Re-Identification Based on Gait Analysis
Shan, Y., Yu, G., & Gao, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3320577 · Cited by 2 · see citing works
Biometric Authentication using Gait Recognition
Gupta, M. (2023) · Universal Research Reports · doi:10.36676/urr.2023-v10i4-001 · Cited by 2 · see citing works
Assessing the Robustness of Deep Learning-Based Gait Recognition Systems Against Adversarial Attacks
Saoudi, E. M., Andaloussi, S. J., & Jaafari, J. (2024) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3659677.3659824 · Cited by 2 · see citing works
Deng, L., Pei, J., Song, Y., Huo, W., Zhang, Y., & Huang, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3550532 · Cited by 2 · see citing works
Research on Multimodal Gait Recognition Based on Silhouette and Skeleton
Chen, M., Li, Z., & He, Y. (2025) · International Conference on Image Processing, Machine Learning and Pattern Recognition (IPMLPR) · doi:10.1145/3759928.3759938 · Cited by 2 · see citing works
Yan, W. Q. (2020) · Texts in computer science · doi:10.1007/978-3-030-61081-4_1 · Cited by 2 · see citing works
Self-citationComparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network
Sadeq, F. E., & Al-Ta’i, Z. T. M. (2023) · Journal of Applied Engineering and Technological Science (JAETS) · doi:10.37385/jaets.v5i1.2806 · Cited by 2 · see citing works
An Enhanced Human Gait Recognition System using UALNN Classifier
Thomas, K. T., & Pushpalatha, K. P. (2024) · IETE Journal of Research · doi:10.1080/03772063.2024.2351563 · Cited by 1 · see citing works
Controllable multi-focal terahertz metalens based on vanadium dioxide
Wu, Y. M., Tao, Y. M., Feng, H., Tian, Y., Gu, M., Han, J., & Yu, M. (2025) · Journal of Modern Optics · doi:10.1080/09500340.2025.2564288 · Cited by 1 · see citing works
Review of Planar Optical System: Lens Based on Metasurfaces
Zeng, L., Tian, Y., & Jing, X. (2025) · Electronics · doi:10.3390/electronics14214322 · Cited by 1 · see citing works
Review on all-dielectric metasurface encryption technology
Wang, Y., Gu, M., Tian, Y., Li, C., Jin, Y., Wang, L., & Jing, X. (2025) · Defence Technology · doi:10.1016/j.dt.2025.11.036 · Cited by 1 · see citing works
GGCN: Gait Recognition with Generate Network and Convolutional Neural Network
Qin, H., Chen, Z., Guo, Q., Wu, Q., & Lu, M. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104790 · Cited by 1 · see citing works
Deep supervised hashing for gait retrieval
Sayeed, M. S., Min, P. P., & Ong, T. S. (2021) · F1000Research · doi:10.12688/f1000research.51368.1 · Cited by 1 · see citing works
Deep supervised hashing for gait retrieval
Sayeed, M. S., Min, P. P., & Ong, T. S. (2022) · F1000Research · doi:10.12688/f1000research.51368.2 · Cited by 1 · see citing works
HPO Using Dwarf Mongoose Optimization in the GAN Model for Human Gait Recognition
Karthik, M. G., Mantena, S. V., Chintamaneni, P., Chennupalli, S., Naladesi, S. Y., & Vatambeti, R. (2023) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.130206 · Cited by 1 · see citing works
Human Action Recognition Based on YOLOv7
Liang, C., & Yan, W. Q. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1738-9.ch006 · Cited by 1 · see citing works
Self-citationPremalatha, G., Chandramani, P. V., & Kathiroli, P. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-210522 · Cited by 1 · see citing works
RetractedDeveloping of a multi-angle and varied walking conditions dataset for human gait recognition
Zazueta, J. M. B., Rodriguez-Rangel, H., Puig, V., Medrano-Diaz, M. A., Gregorio, S. I. F., & Morales-Rosales, L. A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109404 · Cited by 1 · see citing works
Gait Recognition Analysis for Human Identification Analysis- A Hybrid Deep Learning ProcessPreprint
Mathivanan, B., & Perumal, P. (2021) · Research Square · doi:10.21203/rs.3.rs-549846/v1 · Cited by 1 · see citing works
Empirical Analysis for Earlier Diagnosis of Alzheimer’s Disease Using Deep Learning
Sun, S. (2020) · Tuwhera (Auckland University of Technology)
Editorial: A Wonderful Venue for Networking Neuroscience and Computational Intelligence
Jiang, X. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721030052
Yan, W. Q. (2023) · Texts in computer science · doi:10.1007/978-981-99-4823-9_1
Self-citation
- 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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