Open AccessLFN: Based on the Convolutional Neural Network of Gait Recognition Method
Author(s) -
Xiaopeng Zhu,
Lijun Yun,
Feiyan Cheng,
Chunjie Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032075
Subject(s) - silhouette , gait , convolutional neural network , artificial intelligence , computer science , feature (linguistics) , convolution (computer science) , pattern recognition (psychology) , feature extraction , computer vision , artificial neural network , flexibility (engineering) , mathematics , physical medicine and rehabilitation , medicine , statistics , linguistics , philosophy
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.