LFN: Based on the Convolutional Neural Network of Gait Recognition Method

Abstract

In this paper, a novel method based on convolutional neural network (CNN) to address gait recognition was proposed. Gait is a unique biologic feature that the feature almost cannot be altered. Existing gait recognition virtually based on a traditional method such as Gait Energy Image (GEI). GEI has various gait silhouette sequence images and put this image together. Meanwhile, those images must in the same gait period, which lacks of flexibility. To address this issue, a different gait recognition model, called LFN, based on a convolution neural network (CNN) was proposed. The model is composed of three convolution layers in parallel. It can be trained in an end-to-end manner and each sample gait silhouette in the uniform gait period does not to be required. Our working base on the OU-ISIR large population gait dataset. Being based on the result of the experiments, our network can learn the significant features of each sample gait silhouette sequence effectively, at the same time, our model can achieve high and stable accuracy in three experiments.