深圳大学新葡的京集团350vip8888
College of Computer Science and Software Engineering, SZU

RFaceID: Towards RFID-based Facial Recognition

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)

 

Chengwen Luo1    Zhongru Yang1    Xingyu Feng1    Jin Zhang1    Hong Jia2    Jianqiang Li1    Jiawei Wu1    Wen Hu2

1Shenzhen University    2University of New South Wales

 

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.

Fig. 1. Rationale of RFID-based face recognition: unique 3D geometry of human face leads to different multi-path reflections.

 

Fig. 2. multi-path effect of a human face in front of the tag matrix

 

Fig. 3. Differences in phase distributions with different user faces keeping static in front of a 5×7 tag matrix

 

Fig. 5. Temporal patterns of phase ((a),(b)) and RSS ((c),(d)) when users are shaking their faces in front of the tag matrix

 

Fig. 6. System overview of RFaceID

 

Fig. 7. Detecting face recognition events for data segmentation

 

Fig. 8. The experiment setting

 

Fig. 9. DET curve of RFaceID under passive and active attacks

 

Acknowledgements

The authors would like to thank anonymous reviewers for their valuable comments. This work is supported by National Natural Science Foundation of China (61972263, U1713212, 62073225), Natural Science Foundation of Guangdong Province (2019A1515011608), and the Stable Support Plan for Higher Education Institutions in Shenzhen (20200810113310001).

 

Bibtex

@article{luo2021rfaceid,

title={RFaceID: Towards RFID-based Facial Recognition},

author={ Chengwen Luo, Zhongru Yang, Xingyu Feng, Jin Zhang, Hong Jia, Jianqiang Li, Jiawei Wu, Wen Hu },

journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},

volume={5},

number={4},

pages={1--21},

year={2021},

publisher={ACM New York, NY, USA}

}

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