GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

14 citations as recorded by crossref.

1. Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends N. Ganesh et al. https://doi.org/10.32604/cmes.2023.028018
2. RETRACTED: Stacked-gait: A human gait recognition scheme based on stacked autoencoders A. Mehmood et al. https://doi.org/10.1371/journal.pone.0310887
3. Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods A. Parashar et al. https://doi.org/10.1016/j.neucom.2022.07.002
4. Protection of gait data set for preserving its privacy in deep learning pipeline A. Parashar & R. Shekhawat https://doi.org/10.1049/bme2.12093
5. Automatic multi-gait recognition using pedestrian’s spatiotemporal features M. Khan et al. https://doi.org/10.1007/s11227-023-05391-0
6. Pose‐based deep gait recognition A. Sokolova & A. Konushin https://doi.org/10.1049/iet-bmt.2018.5046
7. Methods of Gait Recognition in Video A. Sokolova & A. Konushin https://doi.org/10.1134/S0361768819040091
8. A Survey on Gait Recognition C. Wan et al. https://doi.org/10.1145/3230633
9. Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review A. Parashar et al. https://doi.org/10.1007/s10462-022-10365-4
10. RETRACTED ARTICLE: Multimodal Biometric for Person Identification Using Deep Learning Approach A. Sharma et al. https://doi.org/10.1007/s11277-022-09556-7
11. Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network G. Batchuluun et al. https://doi.org/10.1109/ACCESS.2018.2876890
12. Classification of Subjects With Balance Disorders Using 1D-CNN and Inertial Sensors J. Napieralski et al. https://doi.org/10.1109/ACCESS.2022.3225521
13. Identification of a Person by Gait in a Video Stream M. Uzdiaev et al. https://doi.org/10.21869/2223-1560-2020-24-4-57-75
14. View-invariant gait recognition method by three-dimensional convolutional neural network W. Xing et al. https://doi.org/10.1117/1.JEI.27.1.013010