Article

Poster: Deep Gait Recognition via Millimeter Wave

Authors:

Yingjie Zeng,

Lanshun NieAuthors Info & Claims

EWSN '19: Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks

Pages 254 - 255

Published: 15 March 2019 Publication History

Publisher Site

Abstract

The key to personalizable behavior in smart spaces is knowing where and who a particular person is. However, concerns arise around potential leakage of face/video information, and many people do not accept cameras in their homes or workplaces. With the aid of a deep recurrent network, we propose a human recognition system that identifies gaits based on millimeter wave (MMwave). By a commercial, off-the-shelf radar, our system first obtains sparse point clouds from the reflection profiles of people walking. A deep neural network is then used to extract gait information from sequential point clouds and identify different people. Preliminary results demonstrate that MMwave is a very promising modality for gait recognition.

References

[1]

C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 1(2):4, 2017.

Google Scholar

[2]

T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez Jr. Millimeter wave mobile communications for 5g cellular: It will work! IEEE access, 1(1):335–349, 2013.

Google Scholar

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Information & Contributors

Information

Published In

EWSN '19: Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks

February 2019

436 pages

ISBN:9780994988638

General Chairs:
Yunhao Liu
Tsinghua University
,
Guoliang Xing
The Chinese University of Hong Kong
,
Program Chairs:
Yuan He
Tsinghua University
,
Gian Pietro Picco
University of Trento

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Junction Publishing

United States

Publication History

Published: 15 March 2019

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Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 81 of 195 submissions, 42%

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