RFaceID-Reference-Cited by-同舟云学术

RFaceID

Published:2021-12-27 Issue:4 Volume:5 Page:1-21
ISSN:2474-9567
Container-title:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
language:en
Short-container-title:Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

Author:

Luo Chengwen¹,Yang Zhongru¹,Feng Xingyu¹,Zhang Jin¹,Jia Hong²,Li Jianqiang¹,Wu Jiawei¹,Hu Wen²

Affiliation:

1. Shenzhen University, Shenzhen, China

2. University of New South Wales, Sydney, Australia

Abstract

Face recognition (FR) has been widely used in many areas nowadays. However, the existing mainstream vision-based facial recognition has limitations such as vulnerability to spoofing attacks, sensitivity to lighting conditions, and high risk of privacy leakage, etc. To address these problems, in this paper we take a sparkly different approach and propose RFaceID, a novel RFID-based face recognition system. RFaceID only needs the users to shake their faces in front of the RFID tag matrix for a few seconds to get their faces recognized. Through theoretical analysis and experiment validations, the feasibility of the RFID-based face recognition is studied. Multiple data processing and data augmentation techniques are proposed to minimize the negative impact of environmental noises and user dynamics. A deep neural network (DNN) model is designed to characterize both the spatial and temporal feature of face shaking events. We implement the system and extensive evaluation results show that RFaceID achieves a high face recognition accuracy at 93.1% for 100 users, which shows the potential of RFaceID for future facial recognition applications.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Link

https://dl.acm.org/doi/pdf/10.1145/3494985

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