Gait Recognition under Different Covariate Conditions using Deep Learning Technique

Gait as a biometric has become a popular research topic in recent years as a result of its numerous applications in sectors such as surveillance, authentication, and so on. It is capable of achieving detection at a distance that few other technologies can equal. It is still a difficult problem to solve since real human gait is influenced by several variable elements such as alterations in clothing, speed, and carrying situation. Also, unknown covariate circumstances may impact the training and testing settings for a specific individual in gait recognition. Image sequences are typically used by computer-aided gait recognition systems without taking into account variables such as clothes and the contents of carrier bags while on the move. In this work, we provide a technique for selecting gait energy image-based (GEI) features, that is both effective and robust. The covariate factors have less impact on the given gait representation. A simple ten-layered convolutional neural network (CNN) is designed which intakes GEI as input. Several typical variations and occlusions that impact and worsen gait recognition ability are less susceptible to the suggested method. For both clothing and mobility variations, we used the CASIA datasets to assess our observations. The experimental findings reveal that in numerous circumstances, the deep neural network model created in this study achieved better results when compared with other existing algorithms.