Name
Title | ||
|---|---|---|
Gait-based person identification using 3D LiDAR and long short-term memory deep networks |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/01691864.2020.1793812 | ADVANCED ROBOTICS |
Keywords | DocType | Volume |
Gait recognition,point cloud,convolutional neural network,long short-term memory,data augmentation | Journal | 34.0 |
Issue | ISSN | Citations |
18 | 0169-1864 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroyuki Yamada | 1 | 0 | 0.34 |
| Jeongho Ahn | 2 | 0 | 0.68 |
| Óscar Martínez Mozos | 3 | 429 | 25.82 |
| Yumi Iwashita | 4 | 212 | 23.59 |
| Ryo Kurazume | 5 | 622 | 74.18 |