research-article

VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation

Authors:

Aditya Singh Rathore,

Wenyao XuAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 312 - 325

https://doi.org/10.1145/3384419.3430779

Published: 16 November 2020 Publication History

Abstract

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations during communication. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the text-independent vocal tract and vocal source features and input them to an ensemble classifier for user authentication. VocalPrint is practical as it leverages a low-cost, portable, and energy-efficient hardware allowing effortless transition to a smartphone while having sufficient usability as typical voice authentication systems due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint can achieve over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

References

[1]

Fadel Adib, Hongzi Mao, Zachary Kabelac, Dina Katabi, and Robert C Miller. 2015. Smart homes that monitor breathing and heart rate. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, 837--846.

Digital Library

[2]

M. L. Attiah, M. Ismail, R. Nordin, and N. F. Abdullah. 2015. Dynamic multi-state ultra-wideband mm-wave frequency selection for 5G communication. In 2015 IEEE 12th Malaysia International Conference on Communications (MICC). 219--224.

[3]

Leonard E Baum and John Alonzo Eagon. 1967. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull. Amer. Math. Soc. 73, 3 (1967), 360--363.

[4]

Logan Blue, Hadi Abdullah, Luis Vargas, and Patrick Traynor. 2018. 2ma: Verifying voice commands via two microphone authentication. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security. ACM, 89--100.

Digital Library

[5]

Rudolf Maarten Bolle, Jonathan Hudson Connell, and Nalini K Ratha. 2005. System and method for liveness authentication using an augmented challenge/response scheme. US Patent 6,851,051.

[6]

Niko Brümmer and Edward De Villiers. 2013. The bosaris toolkit: Theory, algorithms and code for surviving the new dcf. arXiv preprint arXiv:1304.2865 (2013).

[7]

Andrew Bud. 2018. Facing the future: The impact of Apple FaceID. Biometric Technology Today 2018, 1 (2018), 5--7.

[8]

Joseph P Campbell. 1997. Speaker recognition: A tutorial. Proc. IEEE 85, 9 (1997), 1437--1462.

[9]

William M Campbell, Joseph P Campbell, Douglas A Reynolds, Elliot Singer, and Pedro A Torres-Carrasquillo. 2006. Support vector machines for speaker and language recognition. Computer Speech & Language 20, 2--3 (2006), 210--229.

[10]

Si Chen, Kui Ren, Sixu Piao, Cong Wang, Qian Wang, Jian Weng, Lu Su, and Aziz Mohaisen. 2017. You can hear but you cannot steal: Defending against voice impersonation attacks on smartphones. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 183--195.

[11]

Jae-Hyun Choi, Jong-Hun Jang, and Jin-Eep Roh. 2015. Design of an FMCW radar altimeter for wide-range and low measurement error. IEEE Transactions on Instrumentation and Measurement 64, 12 (2015), 3517--3525.

[12]

Tarang Chugh, Kai Cao, and Anil K Jain. 2018. Fingerprint spoof buster: Use of minutiae-centered patches. IEEE Transactions on Information Forensics and Security 13, 9 (2018), 2190--2202.

[13]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297.

Digital Library

[14]

Sharmistha Das and John HL Hansen. 2004. Detection of voice onset time (VOT) for unvoived stops (/p/,/t/,/k/) using the Teager energy operator (TEO) for automatic detection of accented English. In Proceedings of the 6th Nordic Signal Processing Symposium, 2004. NORSIG 2004. Citeseer, 344--347.

[15]

TK Das and KM Nahar. 2016. A voice identification system using hidden markov model. Indian Journal of Science and Technology 9, 4 (2016).

[16]

Mangesh S Deshpande and Raghunath S Holambe. 2008. Text-independent speaker identification using hidden Markov models. In 2008 First International Conference on Emerging Trends in Engineering and Technology. IEEE, 641--644.

Digital Library

[17]

Gunnar Fant. 1970. Acoustic theory of speech production: with calculations based on X-ray studies of Russian articulations. Number 2. Walter de Gruyter.

[18]

Huan Feng, Kassem Fawaz, and Kang G Shin. 2017. Continuous authentication for voice assistants. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. 343--355.

Digital Library

[19]

J. Hasch, E. Topak, R. Schnabel, T. Zwick, R. Weigel, and C. Waldschmidt. 2012. Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band. IEEE Transactions on Microwave Theory and Techniques 60, 3 (March 2012), 845--860.

[20]

Roger A Horn. 1990. The hadamard product. In Proc. Symp. Appl. Math, Vol. 40. 87--169.

[21]

Danoush Hosseinzadeh and Sridhar Krishnan. 2007. Combining vocal source and MFCC features for enhanced speaker recognition performance using GMMs. In 2007 IEEE 9th Workshop on Multimedia Signal Processing. IEEE, 365--368.

[22]

Artur Janicki, Federico Alegre, and Nicholas Evans. 2016. An assessment of automatic speaker verification vulnerabilities to replay spoofing attacks. Security and Communication Networks 9, 15 (2016), 3030--3044.

Digital Library

[23]

Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards Environment Independent Device Free Human Activity Recognition. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. ACM, 289--304.

Digital Library

[24]

Ossi Johannes Kaltiokallio, Hüseyin Yigitler, Riku Jäntti, and Neal Patwari. 2014. Non-invasive respiration rate monitoring using a single COTS TX-RX pair. In Proceedings of the 13th international symposium on Information processing in sensor networks. IEEE Press, 59--70.

Digital Library

[25]

James E Kelley, Jr. 1960. The cutting-plane method for solving convex programs. Journal of the society for Industrial and Applied Mathematics 8, 4 (1960), 703--712.

[26]

Lawrence George Kersta. 1962. Voiceprint identification. Nature 196, 4861 (1962), 1253--1257.

[27]

Bernd J Kröger, Georg Schröder, and Claudia Opgen-Rhein. 1995. A gesture-based dynamic model describing articulatory movement data. The Journal of the Acoustical Society of America 98, 4 (1995), 1878--1889.

[28]

Jeffrey C Lagarias, James A Reeds, Margaret H Wright, and Paul E Wright. 1998. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal on optimization 9, 1 (1998), 112--147.

[29]

Selena Larson. 2017. Google Home now recognizes your individual voice. CNN Money, San Francisco, California 3 (2017).

[30]

Changzhi Li, Victor M Lubecke, Olga Boric-Lubecke, and Jenshan Lin. 2013. A review on recent advances in Doppler radar sensors for noncontact healthcare monitoring. IEEE Transactions on microwave theory and techniques 61, 5 (2013), 2046--2060.

[31]

Penghua Li, Fangchao Hu, Yinguo Li, and Yang Xu. 2014. Speaker identification using linear predictive cepstral coefficients and general regression neural network. In Proceedings of the 33rd Chinese Control Conference. IEEE, 4952--4956.

[32]

Jaime Lien, Nicholas Gillian, M Emre Karagozler, Patrick Amihood, Carsten Schwesig, Erik Olson, Hakim Raja, and Ivan Poupyrev. 2016. Soli: Ubiquitous gesture sensing with millimeter wave radar. ACM Transactions on Graphics (TOG) 35, 4 (2016), 142.

Digital Library

[33]

C. Lin, S. Chang, C. Chang, and C. Lin. 2010. Microwave Human VocalVibration Signal Detection Based on Doppler Radar Technology. IEEE Transactions on Microwave Theory and Techniques 58, 8 (Aug 2010), 2299--2306.

[34]

Feng Lin, Chen Song, Yan Zhuang, Wenyao Xu, Changzhi Li, and Kui Ren. 2017. Cardiac scan: A non-contact and continuous heart-based user authentication system. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. ACM, 315--328.

Digital Library

[35]

Rui Liu, Cory Cornelius, Reza Rawassizadeh, Ronald Peterson, and David Kotz. 2018. Vocal resonance: Using internal body voice for wearable authentication. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 19.

Digital Library

[36]

Bram Lohman, Olga Boric-Lubecke, VM Lubecke, PW Ong, and MM Sondhi. 2002. A digital signal processor for Doppler radar sensing of vital signs. IEEE Engineering in Medicine and Biology Magazine 21, 5 (2002), 161--164.

[37]

Judith A Markowitz. 2000. Voice biometrics. Commun. ACM 43, 9 (2000), 66--73.

Digital Library

[38]

Alvin F Martin and Mark A Przybocki. 2001. The NIST speaker recognition evaluations: 1996--2001. In 2001: A Speaker Odyssey-The Speaker Recognition Workshop.

[39]

Jack McLaughlin, Douglas A Reynolds, and Terry Gleason. 1999. A study of computation speed-ups of the GMM-UBM speaker recognition system. In Sixth European Conference on Speech Communication and Technology.

[40]

Ian Vince McLoughlin. 2008. Line spectral pairs. Signal processing 88, 3 (2008), 448--467.

[41]

Yan Meng, Zichang Wang, Wei Zhang, Peilin Wu, Haojin Zhu, Xiaohui Liang, and Yao Liu. 2018. WiVo: Enhancing the Security of Voice Control System via Wireless Signal in IoT Environment. In Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. ACM, 81--90.

Digital Library

[42]

K Sri Rama Murtty and Bayya Yegnanarayana. 2005. Combining evidence from residual phase and MFCC features for speaker recognition. IEEE signal processing letters 13, 1 (2005), 52--55.

[43]

Seiichi Nakagawa, Kouhei Asakawa, and Longbiao Wang. 2007. Speaker recognition by combining MFCC and phase information. In Eighth annual conference of the international speech communication association.

[44]

National Instruments [n.d.]. mmWave Transceiver System. http://www.ni.com/sdr/mmwave/

[45]

NXP [n.d.]. S32R27 Reference Design Kit for high-performance Automotive Radar. https://www.nxp.com/products/power-management/system-basis-chips/functional-safety-sbcs/s32r27-reference-design-kit-for-high-performance-automotive-radar:RDK-S32R274

[46]

J. D. Park and W. J. Kim. 2006. An Efficient Method of Eliminating the Range Ambiguity for a Low-Cost FMCW Radar Using VCO Tuning Characteristics. IEEE Transactions on Microwave Theory and Techniques 54, 10 (Oct 2006), 3623--3629.

[47]

Hemant A Patil and Pallavi N Baljekar. 2012. Classification of normal and pathological voices using TEO phase and Mel cepstral features. In 2012 International Conference on Signal Processing and Communications (SPCOM). IEEE, 1--5.

[48]

Douglas T Petkie, Erik Bryan, Carla Benton, and Brian D Rigling. 2009. Millimeter-wave radar systems for biometric applications. In Millimetre Wave and Terahertz Sensors and Technology II, Vol. 7485. International Society for Optics and Photonics, 748502.

[49]

Michael David Plumpe, Thomas F Quatieri, and Douglas A Reynolds. 1999. Modeling of the glottal flow derivative waveform with application to speaker identification. IEEE Transactions on Speech and Audio Processing 7, 5 (1999), 569--586.

[50]

Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukas Burget, Ondrej Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlicek, Yanmin Qian, Petr Schwarz, et al. 2011. The Kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding. IEEE Signal Processing Society.

[51]

Jianwei Qian, Haohua Du, Jiahui Hou, Linlin Chen, Taeho Jung, and Xiang-Yang Li. 2018. Hidebehind: Enjoy Voice Input with Voiceprint Unclonability and Anonymity. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 82--94.

Digital Library

[52]

Alain Rakotomamonjy, Francis Bach, Stephane Canu, and Yves Grandvalet. 2007. More efficiency in multiple kernel learning. In Proceedings of the 24th international conference on Machine learning. 775--782.

Digital Library

[53]

Ravi P Ramachandran, Mihailo S Zilovic, and Richard J Mammone. 1995. A comparative study of robust linear predictive analysis methods with applications to speaker identification. IEEE transactions on speech and audio processing 3, 2 (1995), 117--125.

[54]

Douglas A Reynolds and Richard C Rose. 1995. Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE transactions on speech and audio processing 3, 1 (1995), 72--83.

[55]

Nirupam Roy and Romit Roy Choudhury. 2016. Listening through a vibration motor. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 57--69.

Digital Library

[56]

Syed Muhammad Saqlain, Muhammad Sher, Faiz Ali Shah, Imran Khan, Muhammad Usman Ashraf, Muhammad Awais, and Anwar Ghani. 2019. Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowledge and Information Systems 58, 1 (2019), 139--167.

Digital Library

[57]

S. Scherr, S. Ayhan, B. Fischbach, A. Bhutani, M. Pauli, and T. Zwick. 2015. An Efficient Frequency and Phase Estimation Algorithm With CRB Performance for FMCW Radar Applications. IEEE Transactions on Instrumentation and Measurement 64, 7 (July 2015), 1868--1875.

[58]

Jiacheng Shang, Si Chen, and Jie Wu. 2018. Defending Against Voice Spoofing: A Robust Software-based Liveness Detection System. In 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 28--36.

[59]

Jiacheng Shang, Si Chen, and Jie Wut. 2018. SRVoice: A Robust Sparse Representation-based Liveness Detection System. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 291--298.

[60]

Robert V Shannon, Fan-Gang Zeng, Vivek Kamath, John Wygonski, and Michael Ekelid. 1995. Speech recognition with primarily temporal cues. Science 270, 5234 (1995), 303--304.

[61]

Jan SilovskᏳ and Jan Nouza. 2006. Speech, speaker and speaker's gender identification in automatically processed broadcast stream. Radioengineering (2006).

[62]

J Singh, B Ginsburg, S Rao, and K Ramasubramanian. 2017. AWR1642 mm-Wave sensor: 76--81-GHz radar-on-chip for short-range radar applications. Texas Instruments (2017), 1--7.

[63]

Craig S. Smith. [n.d.]. Alexa and Siri Can Hear This Hidden Command. You Can't. (Published 2018). http://www.nytimes.com/2018/05/10/technology/alexa-siri-hidden-command-audio-attacks.html

[64]

synopsys [n.d.]. High-Performance DSP and Control Processing for Complex 5G Requirements. https://www.synopsys.com/designware-ip/technical-bulletin/high-performance-dsp-for-5g-dwtb-q418.html

[65]

Guochao Wang, Jose-Maria Munoz-Ferreras, Changzhan Gu, Changzhi Li, and Roberto Gómez-García. 2014. Application of linear-frequency-modulated continuous-wave (LFMCW) radars for tracking of vital signs. IEEE transactions on microwave theory and techniques 62, 6 (2014), 1387--1399.

[66]

Jianglin Wang. 2013. Physiologically-motivated feature extraction methods for speaker recognition. (2013).

[67]

Qian Wang, Xiu Lin, Man Zhou, Yanjiao Chen, Cong Wang, Qi Li, and Xiangyang Luo. 2019. VoicePop: A pop noise based anti-spoofing system for voice authentication on smartphones. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2062--2070.

Digital Library

[68]

Teng Wei, Shu Wang, Anfu Zhou, and Xinyu Zhang. 2015. Acoustic eavesdropping through wireless vibrometry. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 130--141.

Digital Library

[69]

Chenhan Xu, Zhengxiong Li, Hanbin Zhang, Aditya Singh Rathore, Huining Li, Chen Song, Kun Wang, and Wenyao Xu. 2019. WaveEar: Exploring a mmWave-based Noise-resistant Speech Sensing for Voice-User Interface. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 14--26.

Digital Library

[70]

Y. Xu, S. Wu, C. Chen, J. Chen, and G. Fang. 2012. A Novel Method for Automatic Detection of Trapped Victims by Ultrawideband Radar. IEEE Transactions on Geoscience and Remote Sensing 50, 8 (Aug 2012), 3132--3142.

[71]

Chen Yan, Yan Long, Xiaoyu Ji, and Wenyuan Xu. 2019. The Catcher in the Field: A Fieldprint based Spoofing Detection for Text-Independent Speaker Verification. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 1215--1229.

Digital Library

[72]

Zhicheng Yang, Parth H Pathak, Yunze Zeng, Xixi Liran, and Prasant Mohapatra. 2016. Monitoring vital signs using millimeter wave. In Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. ACM, 211--220.

Digital Library

[73]

Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, XiaoFeng Wang, and Carl A Gunter. 2018. Commandersong: A systematic approach for practical adversarial voice recognition. In 27th {USENIX} Security Symposium ({USENIX} Security 18). 49--64.

[74]

Maxim Zhadobov, Nacer Chahat, Ronan Sauleau, Catherine Le Quement, and Yves Le Drean. 2011. Millimeter-wave interactions with the human body: state of knowledge and recent advances. International Journal of Microwave and Wireless Technologies 3, 2 (2011), 237âĂŞ247.

[75]

Guoming Zhang, Chen Yan, Xiaoyu Ji, Tianchen Zhang, Taimin Zhang, and Wenyuan Xu. 2017. Dolphinattack: Inaudible voice commands. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 103--117.

Digital Library

[76]

Linghan Zhang, Sheng Tan, and Jie Yang. 2017. Hearing your voice is not enough: An articulatory gesture based liveness detection for voice authentication. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 57--71.

Digital Library

[77]

Linghan Zhang, Sheng Tan, Jie Yang, and Yingying Chen. 2016. Voicelive: A phoneme localization based liveness detection for voice authentication on smartphones. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 1080--1091.

Digital Library

[78]

Mingmin Zhao, Fadel Adib, and Dina Katabi. 2016. Emotion recognition using wireless signals. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 95--108.

Digital Library

[79]

Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S Jaakkola, and Matt T Bianchi. 2017. Learning sleep stages from radio signals: A conditional adversarial architecture. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 4100--4109.

[80]

Bing Zhou, Jay Lohokare, Ruipeng Gao, and Fan Ye. 2018. EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. ACM, 321--336.

Digital Library

Cited By

Yang BGuo YXu LYan ZChen HXing GJiang X(2025)SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live InteractionsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/37122869:1(1-30)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3712286
Kong HHuang CYu JShen X(2025)A Survey of mmWave Radar-Based Sensing in Autonomous Vehicles, Smart Homes and IndustryIEEE Communications Surveys & Tutorials10.1109/COMST.2024.340955627:1(463-508)Online publication date: Feb-2025
https://doi.org/10.1109/COMST.2024.3409556
Kim YChoi C(2025)Utilization of a hierarchical electrocardiogram classification model for enhanced biometric identificationComputers in Biology and Medicine10.1016/j.compbiomed.2024.109254184(109254)Online publication date: Jan-2025
https://doi.org/10.1016/j.compbiomed.2024.109254
Show More Cited By

Index Terms

VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

Recommendations

Revisiting the Security of Biometric Authentication Systems Against Statistical Attacks
The uniqueness of behavioral biometrics (e.g., voice or keystroke patterns) has been challenged by recent works. Statistical attacks have been proposed that infer general population statistics and target behavioral biometrics against a particular victim. ...
SUPERVOICE: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech
ASIA CCS '22: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security

Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attackers synthesize the voice of a victim or ...
Accuth: Anti-Spoofing Voice Authentication via Accelerometer
SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which makes these systems vulnerable to various acoustic attacks and suffer high-security risks. In this work, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

SenSys '20

Sponsor:

SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems

November 16 - 19, 2020

Virtual Event, Japan

Acceptance Rates

Overall Acceptance Rate 198 of 990 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

59
Total Citations
View Citations
1,356
Total Downloads

Downloads (Last 12 months)180
Downloads (Last 6 weeks)21

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yang BGuo YXu LYan ZChen HXing GJiang X(2025)SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live InteractionsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/37122869:1(1-30)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3712286
Kong HHuang CYu JShen X(2025)A Survey of mmWave Radar-Based Sensing in Autonomous Vehicles, Smart Homes and IndustryIEEE Communications Surveys & Tutorials10.1109/COMST.2024.340955627:1(463-508)Online publication date: Feb-2025
https://doi.org/10.1109/COMST.2024.3409556
Kim YChoi C(2025)Utilization of a hierarchical electrocardiogram classification model for enhanced biometric identificationComputers in Biology and Medicine10.1016/j.compbiomed.2024.109254184(109254)Online publication date: Jan-2025
https://doi.org/10.1016/j.compbiomed.2024.109254
Liu TLin FBa ZLu LQin ZRen KBalzarotti DXu W(2024)MicGuardProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699122(3963-3978)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699122
Sheng YLi JMa YZhang J(2024)HomeOSD: Appliance Operating-Status Detection Using mmWave RadarSensors10.3390/s2409291124:9(2911)Online publication date: 2-May-2024
https://doi.org/10.3390/s24092911
Niaz FZhang JZheng YKhalid MNiaz A(2024)mm-CUR: A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles Using Millimeter-Wave SensorACM Transactions on Sensor Networks10.1145/369497020:6(1-26)Online publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1145/3694970
Jiang CYu SFu JLin CZhu HMa XLi MGuo LShu YLiu JTan RHe YChen J(2024)Behaviors Speak More: Achieving User Authentication Leveraging Facial Activities via mmWave SensingProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699330(169-183)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699330
Liu HLiu XXie XTong XLi K(2024)PmTrackProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314337:4(1-30)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631433
Shin HHuh JKwon BKim ICheon EKim HLee COakley I(2024)SkullID: Through-Skull Sound Conduction based Authentication for SmartglassesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642506(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642506
Han MYang HNi TDuan DRuan MChen YZhang JXu W(2024)mmSign: mmWave-based Few-Shot Online Handwritten Signature VerificationACM Transactions on Sensor Networks10.1145/360594520:4(1-31)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3605945
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten