Security System based on Gait Recognition using Convolutional LSTM

Abstract

Computer vision based human identification by gait has being receiving a lot of attentions Comparing to other biometric methods using like face finger print and iris capturing gait features needs no cooperation from a target person Because imitation of gait is difficult using gait recognition technology on security system can avoid the risks like stolen ID card and password in traditional security system These make gait recognition technology great advantageous in security criminal prevention and suspects tracking This study constructs a human identification system based on gait recognition by using Convolutional Neural Network and Convolutional Long Short-Term Memory Neural Network The feasibility of the system is verified by an experimental security system Beside gait differences walking speed may be different between people This study uses a CNN for extracting gait spatial features and a ConvLSTM for capturing temporal features Using a 30 FPS camera to obtain raw video of a walking person the system uses image background subtraction to obtain the silhouettes of the person for every time step Silhouettes of one walking cycle is used to represent the gait of a person and is input to a neural network In the neural network convolutional layers will first extract local spatial features and the ConvLSTM layer will further extract spatio-temporal features The output sequential feature maps will be converted to a fixed size vector by temporal-pooling and will be compared in the similarity with all data in the database to find out the person’s identity The CNN and ConvLSTM layers are jointly trained using the Siamese architecture The OU-ISIR Large Population dataset is split into a training subset and a testing subset The model is proved to have identification accuracy over 80% under the scale of a thousand people The result confirms the method in this study is effective Except using the large dataset to verify the neural network model this study also implements an experimental security system to verify the feasibility of the practical applications of the system

Date of Award	2019
Original language	English
Supervisor	Tzone-I Wang (Supervisor)