Thermal Gait Dataset for Deep Learning-Oriented Gait Recognition.

This study attempted to construct a thermal dataset of human gait in diverse environments suitable for building and evaluating sophisticated deep learning models (e.g., vision transformers) for gait recognition. Gait is a behavioral biometric to identify a person and requires no cooperation from the person, making it suitable for security and surveillance applications. For security purposes, it is desirable to be able to recognize a person in darkness or other inadequate lighting conditions, in which thermal imagery is advantageous over visible light imagery. Despite the importance of such nighttime person identification, available thermal gait datasets captured in the dark are scarce. This study, therefore, collected a relatively large set of thermal gait data in both indoor and outdoor environments with several walking styles, e.g., walking normally, walking while carrying a bag, and walking fast. This dataset was utilized in multiple gait recognition tasks, such as gender classification and person verification, using legacy convolutional neural networks (CNNs) and modern vision transformers (ViTs). Experiments using this dataset revealed the effective training method for person verification, the effectiveness of ViT on gait recognition, and the robustness of the models against the difference in walking styles; it suggests that the developed dataset enables various studies on gait recognition using state-of-the-art deep learning models.