1 INTRODUCTION

2 THEORETICAL BACKGROUND  
2.1 Biometric Identification  
2.2 Deep Learning Approaches Considered for Gait Recognition  
2.3 Gait Recognition  

3 GAIT RECOGNITION THROUGH DEEP LEARNING-BASED APPROACHES  
3.1 Convolutional Neural Networks  
3.2 Capsule Networks  
3.3 Recurrent Neural Networks  
3.4 Autoencoders  
3.5 Deep Belief Networks  
3.6 Generative Adversarial Networks  
3.7 Discussion  

4 DATASETS  
4.1 CMU MoBo Dataset  
4.2 TUM GAID Dataset  
4.3 HID-UMD Dataset  
4.4 CASIA  
4.5 OU-ISIR Biometric Database  
4.6 University of South Florida Dataset  
4.7 Southampton Dataset  
4.8 AVA Multi-View Dataset for Gait Recognition (AVAMVG)  
4.9 Kyushu University 4D Gait Database  
4.10 WhuGAIT Datasets  
4.11 Datasets in Usage Contexts  

5 CONCLUSIONS AND FUTURE DIRECTIONS