Gait Recognition based on Improved LeNet Network

Today, biometrics play an important role in personal identification systems, but there are also many problems. For example, face recognition is susceptible to lighting, makeup, age and distance, and fingerprints are easily forged. Gait recognition is to identify the identity through the walking posture of the person. The gait features are characterized by long-distance, non-contact and non-disguise, which can overcome the defects of face and fingerprint features. However, gait recognition is susceptible to covariates such as clothing, backpacks, and perspectives. In order to reduce the influence of covariates on gait recognition, this paper conducts an in-depth study on gait recognition algorithms. The main contents are as follows: for the residual network model, not only can the network convergence speed be accelerated, but also the recognition rate can be improved. Therefore, the idea of the residual network is combined into the LeNet network model to improve the LeNet network model. Verify the performance of the improved LeNet network model, the improved LeNet network model improved the recognition rate of normal, backpack and coat walking by 0.3%, 0.5%, and 0.8% respectively. Therefore, the improved LeNet network model makes the network converge faster and the recognition rate is improved to some extent.