Gait-based person identification using 3D LiDAR and long short-term memory deep networks. (16th September 2020)

• Record Type: Journal Article
• Authors: Yamada, Hiroyuki; Ahn, Jeongho; Mozos, Oscar Martinez; Iwashita, Yumi; Kurazume, Ryo
• Is Part Of: Advanced robotics. Volume 34:Number 18(2020)
• Page Start: 1201
• Page End: 1211
• Publication Date: 2020-09-16
• Subjects: Gait recognition -- point cloud -- convolutional neural network -- long short-term memory -- data augmentation; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1793812
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Gait recognition is one measure of biometrics, which also includes facial, fingerprint, and retina recognition. Although most biometric methods require direct contact between a device and a subject, gait recognition has unique characteristics whereby interaction with the subjects is not required and can be performed from a distance. Cameras are commonly used for gait recognition, and a number of researchers have used depth information obtained using an RGB-D camera, such as the Microsoft Kinect. Although depth-based gait recognition has advantages, such as robustness against light conditions or appearance variations, there are also limitations. For instance, the RGB-D camera cannot be used outdoors and the measurement distance is limited to approximately 10 meters. The present paper describes a long short-term memory-based method for gait recognition using a real-time multi-line LiDAR. Very few studies have dealt with LiDAR-based gait recognition, and the present study is the first attempt that combines LiDAR data and long short-term memory for gait recognition and focuses on dealing with different appearances. We collect the first gait recognition dataset that consists of time-series range data for 30 people with clothing variations and show the effectiveness of the proposed approach. GRAPHICAL ABSTRACT: UF0001