Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar_Journal of Biomedical Engineering

Authors：

HUANG Wei ¹ , REN Wei ² , WANG Kehan ¹ , LI Zhao ³ , WANG Jianqi ³ , LU Guohua ³ ,  QI Fugui ³

1. School of Basic Medical Science, Fourth Military Medical University, Xi’an 710032, P. R. China;
2. Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, P. R. China;
3. School of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, P. R. China;

Corresponding author：

QI Fugui, Email: qifgbme@outlook.com

Keywords：

Microwave heartprint; Bio-radar; Heart micro-motion; Non-contact; Human identification

DOI：

10.7507/1001-5515.202309068

Video：

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Abstract Full text Figures/Tables Video References Cited by

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

Citation： HUANG Wei, REN Wei, WANG Kehan, LI Zhao, WANG Jianqi, LU Guohua, QI Fugui. Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar. Journal of Biomedical Engineering, 2024, 41(2): 272-280. doi: 10.7507/1001-5515.202309068 Copy

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11.	Qi F, Lv H, Wang J, et al. Quantitative evaluation of channel micro-doppler capacity for MIMO UWB radar human activity signals based on time-frequency signatures. IEEE T Geosci Remote, 2020, 58(9): 6138-6151.
12.	Ren W, Qi F, Foroughian F, et al. Vital sign detection in any orientation using a distributed radar network via modified independent component analysis. IEEE T Microw Theory, 2021, 69(11): 4774-4790.
13.	Hui X, Kan E C. Monitoring vital signs over multiplexed radio by near-field coherent sensing. Nat Electron, 2018, 1(1): 74-78.
14.	Qiao J, Qi F, Liang F, et al. Contactless multiscale measurement of cardiac motion using biomedical radar sensor. Front Cardiovasc Med, 2022, 9: 1057195.
15.	Rissacher D, Galy D. Cardiac radar for biometric identification using nearest neighbour of continuous wavelet transform peaks// IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). Hong Kong: IEEE, 2015: 1-6.
16.	Zhuang Y, Song C, Lin F, et al. On the feasibility of non-contact cardiac motion sensing for emerging heart-based biometrics// 2016 IEEE Radio and Wireless Symposium (RWS). Austin: IEEE, 2016: 204-206.
17.	Shi K, Will C, Weigel R, et al. Contactless person identification using cardiac radar signals// 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Houston: IEEE, 2018: 1-6.
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19.	蹇强. 遮蔽场景穿墙雷达人体行为识别方法研究. 成都: 电子科技大学, 2022.
20.	Cao P, Xia W, Li Y. Heart ID: Human identification based on radar micro-doppler signatures of the heart using deep learning. Remote Sens, 2019, 11(10): 1220.

1. Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE T Circ Syst Vid, 2004, 14(1): 4-20.
2. 孙冬梅, 裘正定. 生物特征识别技术综述. 电子学报, 2001, 29(S1): 1744.
3. Jonsson K, Kittler J, Li Y P. Support vector machines for face authentication. Image Vision Comput, 2002, 20(5/6): 369-375.
4. Jin Z, Lim M, Teoh A B J, et al. Generating fixed-length representation from minutiae using kernel methods for fingerprint authentication. IEEE T Syst Man Cy A, 2016, 46(10): 1415-1428.
5. Thomas T, George A, Devi K P I. Effective iris recognition system. Pro Tech, 2016, 25: 464-472.
6. Farke F M, Lassak L, Pinter J, et al. Exploring user authentication with windows hello in a small business environment// Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022). Boston: USENIX Association, 2022: 523-540.
7. Mock K, Hoanca B, Weaver J, et al. Real-time continuous iris recognition for authentication using an eye tracker// Proceedings of the 2012 ACM conference on Computer and communications security. Raleigh North Carolina: Association for Computing Machinery, 2012: 1007-1009.
8. Pinto P, Patrão B, Santos H. Free typed text using keystroke dynamics for continuous authentication// De Decker B, Zúquete A. Communications and Multimedia Security. CMS 2014. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2014, 8735: 33-45.
9. Cantoni V, Galdi C, Nappi M, et al. GANT: Gaze analysis technique for human identification. Pattern Recog, 2015, 48(4): 1027-1038.
10. Zhang Z, Wang H, Vasilakos A V, et al. ECG-cryptography and authentication in body area networks. IEEE T Inf Technol B, 2012, 16(6): 1070-1078.
11. Qi F, Lv H, Wang J, et al. Quantitative evaluation of channel micro-doppler capacity for MIMO UWB radar human activity signals based on time-frequency signatures. IEEE T Geosci Remote, 2020, 58(9): 6138-6151.
12. Ren W, Qi F, Foroughian F, et al. Vital sign detection in any orientation using a distributed radar network via modified independent component analysis. IEEE T Microw Theory, 2021, 69(11): 4774-4790.
13. Hui X, Kan E C. Monitoring vital signs over multiplexed radio by near-field coherent sensing. Nat Electron, 2018, 1(1): 74-78.
14. Qiao J, Qi F, Liang F, et al. Contactless multiscale measurement of cardiac motion using biomedical radar sensor. Front Cardiovasc Med, 2022, 9: 1057195.
15. Rissacher D, Galy D. Cardiac radar for biometric identification using nearest neighbour of continuous wavelet transform peaks// IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). Hong Kong: IEEE, 2015: 1-6.
16. Zhuang Y, Song C, Lin F, et al. On the feasibility of non-contact cardiac motion sensing for emerging heart-based biometrics// 2016 IEEE Radio and Wireless Symposium (RWS). Austin: IEEE, 2016: 204-206.
17. Shi K, Will C, Weigel R, et al. Contactless person identification using cardiac radar signals// 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Houston: IEEE, 2018: 1-6.
18. Lin F, Song C, Zhuang Y, et al. Cardiac scan: A non-contact and continuous heart-based user authentication system// Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. New York: Association for Computing Machinery, 2017: 315-328.
19. 蹇强. 遮蔽场景穿墙雷达人体行为识别方法研究. 成都: 电子科技大学, 2022.
20. Cao P, Xia W, Li Y. Heart ID: Human identification based on radar micro-doppler signatures of the heart using deep learning. Remote Sens, 2019, 11(10): 1220.

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