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Sensors 2018, 18(5), 1525;

Biometric Authentication Using the PPG: A Long-Term Feasibility Study

Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain
Author to whom correspondence should be addressed.
Received: 12 April 2018 / Revised: 4 May 2018 / Accepted: 9 May 2018 / Published: 11 May 2018
(This article belongs to the Section Biosensors)
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The photoplethysmogram (PPG) is a biomedical signal that can be used to estimate volumetric blood flow changes in the peripheral circulation. During the past few years, several works have been published in order to assess the potential for PPGs to be used in biometric authentication systems, but results are inconclusive. In this paper we perform an analysis of the feasibility of using the PPG as a realistic biometric alternative in the long term. Several feature extractors (based on the time domain and the Karhunen–Loève transform) and matching metrics (Manhattan and Euclidean distances) have been tested using four different PPG databases (PRRB, MIMIC-II, Berry, and Nonin). We show that the false match rate (FMR) and false non-match rate (FNMR) values remain constant in different time instances for a selected threshold, which is essential for using the PPG for biometric authentication purposes. On the other hand, obtained equal error rate (EER) values for signals recorded during the same session range from 1.0% for high-quality signals recorded in controlled conditions to 8% for those recorded in conditions closer to real-world scenarios. Moreover, in certain scenarios, EER values rise up to 23.2% for signals recorded over different days, signaling that performance degradation could take place with time. View Full-Text
Keywords: photoplethysmogram (PPG); authentication; biometrics; long-term; multi-cycle template; Manhattan distance photoplethysmogram (PPG); authentication; biometrics; long-term; multi-cycle template; Manhattan distance

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Sancho, J.; Alesanco, Á.; García, J. Biometric Authentication Using the PPG: A Long-Term Feasibility Study. Sensors 2018, 18, 1525.

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