Gait is an essential biomedical feature that distinguishes individuals through walking. This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications. However, there is still a lack of sufficient accuracy of gait recognition at night, in addition to taking some critical factors that affect the performances of the recognition algorithm. Therefore, a novel approach is proposed to automatically identify individuals from thermal infrared ( TIR) images according to their gaits captured at night. This approach uses a new night gait network ( NGaitNet) based on similarity deep convolutional neural networks ( CNNs ) method to enhance gait recognition at night. First, the TIR image is represented via personal movements and enhanced body skeleton segments. Then, the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′ angles. These features are trained to identify the most discriminating gait patterns that indicate a change in human identity. To verify the proposed method, the experimental results are performed by using learning and validation curves via being connected by the Visdom website. The proposed thermal infrared imaging night gait recognition ( TIRNGaitNet ) approach has achieved the highest gait recognition accuracy rates ( 99. 5％, 97. 0％) , especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset ( CASIA C ) and Donghua University thermal infrared night gait database ( DHU night gait dataset) . On the same dataset, the results of the TIRNGaitNet approach provide the record scores of ( 98. 0％, 87. 0％) under the slow walking condition and ( 94. 0％, 86. 0％) for the quick walking condition.