In this paper, we propose a string matching approach for personal identification and authentication using the lectrocardiogram (ECG). The ECG is an emerging biometric and there is strong evidence that it contains sufficient discriminative information. Most approaches rely on fiducia of different parts of the heartbeat waveform in the ECG. However, non-fiducial approaches have the advantage of not relying critically on the accurate extraction of fiducia. We propose a new non-fiducial method, based on 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. An accuracy of 100% on identification and an average equal error rate (EER) of 0.37% on authentication
are achieved, despite the existence of different stress states.