Abstract
In recent years, Biometric identification has taken a giant leap from objective security access system such as retina scan or a finger print scan to a continuous biometric identification based system and for that a single lead Electrocardiogram (ECG) signal is considered to be a good marker. However the parameters normally considered for biometrics from ECG normally requires several parameters which again depend on a good resting signal. For applications involving on-the-go Biometric identification, such systems do not provide a reliable solution. This paper describes a novel approach to a robust and continuous biometric identification system by obtaining touch based ECG as well as Photoplethesmogram (PPG) signal simultaneously from miBEAT (an open source CE certified innovative platform to develop medical grade systems) and by mapping variability features in real time common to both the signals. By validating the system on 20 healthy individuals, it was found that this system works with minimum limitations and thereby can be considered for a robust biometric identification system where higher security measures are required.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jain A.K, A Ross. S. Prabhakar. An Introduction to Biometric Recognition-IEEE Transactions on circuits and systems for video Technology, 14(1), 2004, 4–20.
Phua K, Chen J, Dat TH, Shue L (2008) Heart sound as a biometric. Pattern Recognit 41: 906–919.
R. Hoekema, G. J. H. Uijen, and A. van Oosterom, “Geometrical aspects of the interindividual variability of multilead ECG recordings,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 5, pp. 551–559, 2001.
B. P. Simon and C. Eswaran, “An ECG classifier designed using modified decision based neural networks,” Computers and Biomedical Research, vol. 30, no. 4, pp. 257–272, 1997.
Agrafioti F (2008) Robust subject recognition using the Electrocardiogram. University of Toronto, Toronto, Canada.
G. Wuebbeler, et al., “Human verification by heart beat signals,” Working Group 8.42, Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany, 2004, http://www.berlin.ptb.de/8/84/842/BIOMETRIE/842biometriee.html.
“A novel feature ranking algorithm for biometric recognition with PPG signals”. Comput Biol Med. 2014 Jun; 49: 1–14. doi:10.1016/j.compbiomed.2014.03.005. Epub 2014 Mar 18Reşit Kavsaoğlu A1, Polat K2, Recep Bozkurt M1.
Acknowledgments
This work was carried out using miBEAT developed by the support of Department of Scientific and Industrial Research (DSIR), Government of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yathav, J., Bailur, A., Goyal, A.K., Abhinav (2017). miBEAT Based Continuous and Robust Biometric Identification System for On-the-Go Applications. In: Modi, N., Verma, P., Trivedi, B. (eds) Proceedings of International Conference on Communication and Networks. Advances in Intelligent Systems and Computing, vol 508. Springer, Singapore. https://doi.org/10.1007/978-981-10-2750-5_28
Download citation
DOI: https://doi.org/10.1007/978-981-10-2750-5_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2749-9
Online ISBN: 978-981-10-2750-5
eBook Packages: EngineeringEngineering (R0)