rapid-communication

Photoplethysmographic biometrics: : A comprehensive survey

Authors:

Ruggero Donida Labati,

Vincenzo Piuri,

Francesco Rundo,

Fabio ScottiAuthors Info & Claims

Volume 156, Issue C

Pages 119 - 125

https://doi.org/10.1016/j.patrec.2022.03.006

Published: 01 April 2022 Publication History

Highlights

•

The first literature review on PPG-based biometric recognition approaches.

•

The paper describes application scenarios and acquisition techniques.

•

The paper presents a classification of state-of-the-art methods.

•

The paper summarizes the performance of state-of-the-art methods.

•

The paper describes open research problems.

Abstract

The wide diffusion of wearable sensors and mobile devices encouraged the study of biometric recognition techniques that require a low level of cooperation from users. Among them, the analysis of cardiac information extracted from plethysmographic (PPG) signals is attracting the research community due to the possibility of performing continuous authentications using low-cost devices that can acquire signals without any action required from the users. Although PPG-based biometric systems are relatively recent technologies, machine learning techniques and deep learning strategies have shown accuracy in heterogeneous application scenarios. This paper presents the first literature review of PPG-based biometric recognition approaches. First, we describe the application contexts suitable for PPG-based biometrics. Second, we analyze the systems in the literature, describe the acquisition sensors, and present a classification of the processing methods. Third, we summarize the available public datasets and the results achieved by recent state-of-the-art approaches. Finally, we analyze the open problems in this research field.

References

[1]

E. Maiorana, P. Campisi, N. Gonzalez-Carballo, A. Neri, Keystroke dynamics authentication for mobile phones, Proc. of the ACM Symp. on Applied Computing, 2011, pp. 21–26.

[2]

R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, A Scheme for Fingerphoto Recognition in Smartphones, Springer Int. Publishing, Cham, 2019, pp. 49–66.

[3]

A.A. Ross, K. Nandakumar, A.K. Jain, Handbook of Multibiometrics, Springer Publishing Company, Incorporated, 2011.

[4]

F. Rundo, F. Trenta, R. Leotta, C. Spampinato, V. Piuri, S. Conoci, R.D. Labati, F. Scotti, S. Battiato, Advanced temporal dilated convolutional neural network for a robust car driver identification, in: A. Del Bimbo, R. Cucchiara, S. Sclaroff, G.M. Farinella, T. Mei, M. Bertini, H.J. Escalante, R. Vezzani (Eds.), Pattern Recognition. ICPR Int. Workshops and Challenges, Springer Int. Publishing, Cham, 2021, pp. 184–199.

[5]

A.S. Rathore, Z. Li, W. Zhu, Z. Jin, W. Xu, A survey on heart biometrics, ACM Comput. Surv. 53 (6) (2020) 1–38.

[6]

R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, F. Scotti, Deep-ECG: convolutional neural networks for ECG biometric recognition, Pattern Recognit. Lett. 126 (2019) 78–85.

[7]

D. Castaneda, A. Esparza, M. Ghamari, C. Soltanpur, H. Nazeran, A review on wearable photoplethysmography sensors and their potential future applications in health care, Int. J. Biosens. Bioelectron. 4 (2018) 195–202.

[8]

A.N. Uwaechia, D.A. Ramli, A comprehensive survey on ECG signals as new biometric modality for human authentication: recent advances and future challenges, IEEE Access 9 (2021) 97760–97802.

[9]

J. Ribeiro Pinto, J.S. Cardoso, A. Lourenco, Evolution, current challenges, and future possibilities in ECG biometrics, IEEE Access 6 (2018) 34746–34776.

[10]

M. Merone, P. Soda, M. Sansone, C. Sansone, ECG databases for biometric systems: a systematic review, Expert Syst. Appl. 67 (2017) 189–202.

Digital Library

[11]

F. Agrafioti, J. Gao, D. Hatzinakos, Heart biometrics: theory, methods and applications, in: J. Yang (Ed.), Biometrics, IntechOpen, Rijeka, 2011.

[12]

B. Lin, Z. Ma, M. Atef, L. Ying, G. Wang, Low-power high-sensitivity photoplethysmography sensor for wearable health monitoring system, IEEE Sens. J. 21 (14) (2021) 16141–16151.

[13]

N. Nithya, G. Nallavan, Role of wearables in sports based on activity recognition and biometric parameters: a survey, 2021 Int. Conf. on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 1700–1705.

[14]

F. Rundo, C. Spampinato, S. Conoci, Ad-hoc shallow neural network to learn hyper filtered photoplethysmographic (PPG) signal for efficient car-driver drowsiness monitoring, Electronics 8 (8) (2019).

[15]

K. Tyapochkin, E. Smorodnikova, P. Pravdin, Smartphone PPG: signal processing, quality assessment, and impact on HRVparameters, Proc. of the 41st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 4237–4240.

[16]

J. Spooren, D. Preuveneers, W. Joosen, PPG2Live: using dual PPG for active authentication and liveness detection, Proc. of the Int. Conf. on Biometrics (ICB), 2019, pp. 1–6.

[17]

J.P. Vital, M.S. Couceiro, N.M.M. Rodrigues, C.M. Figueiredo, N.M.F. Ferreira, Fostering the NAO platform as an elderly care robot, 2013 IEEE 2nd International Conference on Serious Games and Applications for Health (SeGAH), 2013, pp. 1–5.

[18]

A.F. Abate, C. Bisogni, L. Cascone, A. Castiglione, G. Costabile, I. Mercuri, Social robot interactions for social engineering: opportunities and open issues, 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2020, pp. 539–547.

[19]

A. Bonissi, R. Donida Labati, L. Perico, R. Sassi, F. Scotti, L. Sparagino, A preliminary study on continuous authentication methods for photoplethysmographic biometrics, Proc. of the IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS), 2013, pp. 28–33.

[20]

J. Sancho, Á.A. Iglesias, J. García, Biometric authentication using the PPG: along-term feasibility study, Sensors 18 (5) (2018) 1525.

[21]

J. Sancho, A. Alesanco, J. Garca, Photoplethysmographic authentication in long-term scenarios: a preliminary assessment, in: H. Eskola, O. Väisänen, J. Viik, J. Hyttinen (Eds.), EMBEC & NBC 2017, 2018, pp. 1085–1088.

[22]

A. Walia, A. Kaul, Human recognition via PPG signal using temporal correlation, Proc. of the 5th Int. Conf. on Signal Processing, Computing and Control (ISPCC), 2019, pp. 144–147.

[23]

P. Faragó, R. Groza, L. Ivanciu, S. Hintea, A correlation-based biometric identification technique for ECG, PPG and EMG, Proc. of the 42nd Int. Conf. on Telecommunications and Signal Processing (TSP), 2019, pp. 716–719.

[24]

N. Karimian, M. Tehranipoor, D. Forte, Non-fiducial PPG-based authentication for healthcare application, Proc. of the IEEE EMBS Int. Conf. on Biomedical Health Informatics (BHI), 2017, pp. 429–432.

[25]

R. Donida Labati, V. Piuri, F. Rundo, F. Scotti, C. Spampinato, Biometric recognition of PPGcardiac signals using transformed spectrogram images, in: A. Del Bimbo, R. Cucchiara, S. Sclaroff, G.M. Farinella, T. Mei, M. Bertini, H.J. Escalante, R. Vezzani (Eds.), Pattern Recognition. ICPR Int. Workshops and Challenges, Springer Int. Publishing, Cham, 2021, pp. 244–257.

[26]

J. Luque, G. Cortès, C. Segura, A. Maravilla, J. Esteban, J. Fabregat, END-to-END Photopleth YsmographY (PPG) based biometric authentication by using convolutional neural networks, Proc. of the 26th European Signal Processing Conf. (EUSIPCO), 2018, pp. 538–542.

[27]

D.Y. Hwang, B. Taha, D.S. Lee, D. Hatzinakos, Evaluation of the time stability and uniqueness in PPG-based biometric system, IEEE Trans. Inf. Forensics Secur. 16 (2021) 116–130.

[28]

N.A.L. Jaafar, K.A. Sidek, S.N.A. Mohd Azam, Acceleration plethysmogram based biometric identification, Proc. of the Int. Conf. on BioSignal Analysis, Processing and Systems (ICBAPS), 2015, pp. 16–21.

[29]

S. Chakraborty, S. Pal, Photoplethysmogram signal based biometric recognition using linear discriminant classifier, Proc. of the 2nd Int. Conf. on Control, Instrumentation, Energy Communication (CIEC), 2016, pp. 183–187.

[30]

M.U. Khan, S. Aziz, S.Z. Hassan Naqvi, A. Zaib, A. Maqsood, Pattern analysis towards human verification using photoplethysmograph signals, Proc. of the Int. Conf. on Emerging Trends in Smart Technologies (ICETST), 2020, pp. 1–6.

[31]

S.P.M. Namini, S. Rashidi, Implementation of artificial features in improvement of biometrics based PPG, Proc. of the 6th Int. Conf. on Computer and Knowledge Engineering (ICCKE), 2016, pp. 342–346.

[32]

L. Everson, D. Biswas, M. Panwar, D. Rodopoulos, A. Acharyya, C.H. Kim, C. Van Hoof, M. Konijnenburg, N. Van Helleputte, BiometricNet: deep learning based biometric identification using wrist-worn PPG, Proc. of the IEEE Int. Symp. on Circuits and Systems (ISCAS), 2018, pp. 1–5.

[33]

D. Biswas, L. Everson, M. Liu, M. Panwar, B. Verhoef, S. Patki, C.H. Kim, A. Acharyya, C. Van Hoof, M. Konijnenburg, N. Van Helleputte, CorNET: deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment, IEEE Trans. Biomed. Circuits Syst. 13 (2) (2019) 282–291.

[34]

J. Zbilut, C. Webber, Wiley Encyclopedia of Biomedical Engineering, 2006.

[35]

A. Chandrasekhar, M. Yavarimanesh, K. Natarajan, J.-O. Hahn, R. Mukkamala, PPG sensor contact pressure should be taken into account for cuff-less blood pressure measurement, IEEE Trans. Biomed. Eng. 67 (11) (2020) 3134–3140.

[36]

R.P. Dresher, Y. Mendelson, Reflectance forehead pulse oximetry: effects of contact pressure during walking, Proc. of the Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 3529–3532.

[37]

S.A. Fattah, M.M. Rahman, N. Mustakin, M.T. Islam, A.I. Khan, C. Shahnaz, Wrist-card: PPGsensor based wrist wearable unit for low cost personalized cardio healthcare system, Proc. of the IEEE Global Humanitarian Technology Conf. (GHTC), 2017, pp. 1–7.

[38]

A. Pedrana, D. Comotti, V. Re, G. Traversi, Development of a wearable in-ear PPGsystem for continuous monitoring, IEEE Sens. J. 20 (23) (2020) 14482–14490.

[39]

D.J. McDuff, J.R. Estepp, A.M. Piasecki, E.B. Blackford, A survey of remote optical photoplethysmographic imaging methods, Proc. of the 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 6398–6404.

[40]

M.A. Haque, K. Nasrollahi, T.B. Moeslund, Heartbeat signal from facial video for biometric recognition, in: R.R. Paulsen, K.S. Pedersen (Eds.), Image Analysis, Springer Int. Publishing, Cham, 2015, pp. 165–174.

[41]

O.R. Patil, W. Wang, Y. Gao, W. Xu, Z. Jin, A non-contact PPGbiometric system based on deep neural network, Proc. of the 9th IEEE Int. Conf. on Biometrics Theory, Applications and Systems (BTAS), 2018, pp. 1–7.

[42]

G. Lovisotto, H. Turner, S. Eberz, I. Martinovic, Seeing red: PPG biometrics using smartphone cameras, Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 3565–3574.

[43]

J. Arteaga-Falconi, H. Al Osman, A. El Saddik, R-peak detection algorithm based on differentiation, Proc. of the 9th IEEE Int. Symposium on Intelligent Signal Processing (WISP), 2015, pp. 1–4.

[44]

S.A. Israel, J.M. Irvine, A. Cheng, M.D. Wiederhold, B.K. Wiederhold, ECG to identify individuals, Pattern Recognit. 38 (1) (2005) 133–142.

Digital Library

[45]

Y. Gu, Y. Zhang, Y. Zhang, A novel biometric approach in human verification by photoplethysmographic signals, Proc. of the 4th Int. IEEE EMBS Special Topic Conf. on Information Technology Applications in Biomedicine, 2003, pp. 13–14.

[46]

Y. Gu, Y. Zhang, Photoplethysmographic authentication through fuzzy logic, Proc. of the IEEE EMBS Asian-Pacific Conf. on Biomedical Engineering, 2003., 2003, pp. 136–137.

[47]

N. Karimian, Z. Guo, M. Tehranipoor, D. Forte, Human recognition from photoplethysmography (PPG) based on non-fiducial features, Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 4636–4640.

[48]

U. Yadav, S.N. Abbas, D. Hatzinakos, Evaluation of PPG biometrics for authentication in different states, Proc. of the Int. Conf. on Biometrics (ICB), 2018, pp. 277–282.

[49]

K. Sundararajan, D.L. Woodard, Deep learning for biometrics: a survey, ACM Comput. Surv. 51 (3) (2018) 1–34.

[50]

V. Jindal, J. Birjandtalab, M.B. Pouyan, M. Nourani, An adaptive deep learning approach for PPG-based identification, Proc. of the 38th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp. 6401–6404.

[51]

E. Lee, A. Ho, Y. Wang, C. Huang, C. Lee, Cross-domain adaptation for biometric identification using photoplethysmogram, Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 1289–1293.

[52]

M.A. Haque, K. Nasrollahi, T.B. Moeslund, Real-time acquisition of high quality face sequences from an active pan-tilt-zoom camera, Proc. of the 10th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, 2013, pp. 443–448.

[53]

A.K. Jain, P. Flynn, A.A. Ross, Handbook of Biometrics, first ed., Springer Publishing Company, Incorporated, 2010.

[54]

J. Jang, H. Kim, Performance Measures, Springer US, Boston, MA, 2009, pp. 1062–1068.

[55]

W. Karlen, S. Raman, J.M. Ansermino, G.A. Dumont, Multiparameter respiratory rate estimation from the photoplethysmogram, IEEE Trans. Biomed. Eng. 60 (7) (2013) 1946–1953.

[56]

W. Karlen, M. Turner, E. Cooke, G. Dumont, J.M. Ansermino, CapnoBase: signal database and tools to collect, share and annotate respiratory signals, Annual Meeting of the Society for Technology in Anesthesia (STA), West Palm Beach, 2010.

[57]

M. Villarroel, A. Reisner, G. Clifford, L.-w. Lehman, G. Moody, T. Heldt, T. Kyaw, B. Moody, R. Mark, Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database, Crit. Care Med. 39 (2011) 952–960.

[58]

Z. Zhang, Z. Pi, B. Liu, Troika: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise, IEEE Trans. Biomed. Eng. 62 (2) (2015) 522–531.

[59]

S. Koelstra, C. Muhl, M. Soleymani, J.S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras, Deap: a database for emotion analysis using physiological signals, IEEE Trans. Affect. Comput. 3 (1) (2012) 18–31.

Digital Library

[60]

T. Zhao, Y. Wang, J. Liu, Y. Chen, J. Cheng, J. Yu, TrueHeart: continuous authentication on wrist-worn wearables using PPG-based biometrics, Proc. of the IEEE Conf. on Computer Communications (INFOCOM), 2020, pp. 30–39.

[61]

P. Spachos, Jiexin Gao, D. Hatzinakos, Feasibility study of photoplethysmographic signals for biometric identification, Proc. of the 17th Int. Conf. on Digital Signal Processing (DSP), 2011, pp. 1–5.

Index Terms

Photoplethysmographic biometrics: A comprehensive survey

Index terms have been assigned to the content through auto-classification.

Recommendations

A survey on dorsal hand vein biometrics
Highlights
- A detailed survey on DHV biometrics is carried out.
- A new taxonomy for existing DHV recognition methods has been proposed, which can well reflect the new progress of DHV biometrics.
- The DHV image databases and typical DHV ...
Abstract
Biometrics technology is one of the most important and effective solutions for personal authentication. In recent years, as one of the emerging biometrics technologies, dorsal hand vein (DHV) biometrics has received a lot of attention. In fact, ...
Deep learning techniques for hand vein biometrics: A comprehensive review
Abstract
Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer ...
Highlights
- Hand vein biometrics provide high accuracy, low forgery risk, and non-intrusiveness.
- Review explores DL techniques for FV, PV, DHV, and multimodal vein recognition.
- Covers datasets, SOTA metrics, and best-performing vein ...
Information fusion in biometrics
Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)

User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 156, Issue C

Apr 2022

192 pages

ISSN:0167-8655

Issue’s Table of Contents

Copyright © 2022.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 April 2022

Author Tags

Qualifiers

Rapid-communication

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Feb 2025

Other Metrics

View Author Metrics

Citations

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents