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Personal Identification and Authentication Based on One-Lead ECG Using Ziv-Merhav Cross Parsing

Antão, DPC ; Fred, A. L. N. ; Figueiredo, M. A. T.

Personal Identification and Authentication Based on One-Lead ECG Using Ziv-Merhav Cross Parsing, Proc International Workshop on Pattern Recognition in Information Systems, Funchal, Portugal, Vol. 1, pp. 15 - 24, June, 2010.

Digital Object Identifier:

Abstract
In this paper, we propose a new data compression based ECG biometric
method for personal identification and authentication. The ECG is an emerging
biometric that does not need liveliness verification. There is strong evidence
that ECG signals contain sufficient discriminative information to allow the identification
of individuals from a large population. Most approaches rely on ECG
data and the fiducia of different parts of the heartbeat waveform. However nonfiducial
approaches have proved recently to be also effective, and have the advantage
of not relying critically on the accurate extraction of fiducia. We propose a
non-fiducial method based on the Ziv-Merhav cross parsing algorithm for symbol
sequences (strings). Our method uses a string similarity measure obtained with
a data compression algorithm. We present results on real data, one-lead ECG,
acquired during a concentration task, from 19 healthy individuals, on which our
approach achieves 100% subject identification rate and an average equal error
rate of 1.1% on the authentication task.