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http://dx.doi.org/10.5573/ieie.2017.54.6.100

ECG-based Biometric Authentication Using Random Forest  

Kim, JeongKyun (Department of Computer Software, University of Science and Technology)
Lee, Kang Bok (IoT Research Department, Electronics and Telecommunications Research Institute)
Hong, Sang Gi (IoT Research Department, Electronics and Telecommunications Research Institute)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.6, 2017 , pp. 100-105 More about this Journal
Abstract
This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.
Keywords
Biometric Authentication;
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