Open AccessStudy for Integration of Multi Modal Biometric Personal Identification Using Heart Rate Variability (HRV) Parameter
Author(s) -
Priatna Ahmad Budiman,
Teni Tresnawati,
Ahmad Tossin Alamsyah,
Riandini
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1364/1/012074
Subject(s) - biometrics , heartbeat , heart rate variability , fiducial marker , computer science , artificial intelligence , pattern recognition (psychology) , word error rate , identification (biology) , speech recognition , heart rate , medicine , computer security , botany , biology , blood pressure , radiology
Authentication and Identification is primary part of biometric technology. Currently, electrocardiogram (ECG) is not only being used as a diagnostic tool for clinical purposes, but also as a new biometric tool for high level security system because of its liveliness and uniqueness that is hard to imitate and manipulate. There are many fiducial (signal mark) that is classified from ECG morphology (QRS Complex, P, T waves) has already been researched for this purpose. For non fiducial, many researches are focus on dynamic character from heartbeat (ECG Signal). Heart Rate Variability (HRV) analysis is part of non fiducial classifier. This paper reviews Heart Rate Variability analysis (time and frequency domain) as part of multi matches, one of scenario from multimodal biometric. Sample of person’s heartbeat signal is taken from ECG Database MIT-BIH (MIT and Harvard) and the result of every parameter will be analyzed by Biometric Performance Standards Tools (ISO/IEC IS 19795-1) such as: False Non-Match Rate (FNMR), False Match Rate (FMR) and Thresholds EER (Equal Error Rate). Analysis should show accuracy of multi matches Heart Rate Variability (HRV). As integrator tool, LabView is used to collect offline ECG, process the data and generate HRV Analysis.