Abstract:Gait-based feature recognition is an emerging biometric authentication technology, aiming at analyzing human characteristics such as identity through the walking posture of people. Compared with other biological recognition technologies, gait-based methods have the advantages of being difficult to hide, contactless, and remotely usable. This study designs a single-view gait-based human identity and attributes recognition system under video surveillance. The system uses image processing methods to detect a human gait in real-time from a complex surveillance video. After analyzing with the algorithm trained by deep learning, it can obtain the information of human's identity, gender, and age. Experiments show that the accuracy rate of the system is 98.1%, the accuracy of gender prediction is 97.1%, and the mean absolute error of the age prediction is 6.21, which are better than the traditional benchmark. The system is costless, supporting real-time detection, which can fully meet the needs of small and medium-scale gait research and analysis.