Electrocardiogram (ECG) Biometric Authentication Using Pulse Active Ratio (PAR)

Safie, Sairul Izwan; Soraghan, John J.; Petropoulakis, Lykourgos

doi:10.1109/tifs.2011.2162408

Cited by 105 publications

(46 citation statements)

References 16 publications

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“…There are a few methods that not only require the detection of the R peaks, but also some other characteristic points such as the onset and peak of the P wave, the onset and end of the QRS complex, the peak and end of the T wave [41], [58], [59], [60]. Some methods require the detection of all or a subset of the three major components of each heartbeat (P wave, QRS complex, and T wave) for feature extraction [37], [47], [44].…”

Section: Survey Of Ecg Recognition Methodsmentioning

confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

301

196

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The electrocardiogram (ECG) is an emerging biometric modality that has seen about thirteen years of development, in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our in-house database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG based biometrics perform well, when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 2 equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

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Section: Survey Of Ecg Recognition Methodsmentioning

confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

301

196

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“…Regarding the EID, the value obtained is on par with the results presented in (Zhang and Wei, 2006) for lead I signals (see Table 1), with the added bonus of using a larger database. Figure 5 shows the evolution of the FAR and FRR with the authentication distance threshold, as well as the Receiver Operating Characteristic (ROC) curve, which plots the the TAR against the FAR, highlighting an Area Under ROC (AUR) curve of 95.51%, similar to the one obtained in (Safie et al, 2011). Also of note in Figure 5(a) is the fact that the FAR increases more slowly than the FRR decreases with the threshold.…”

Section: Resultsmentioning

confidence: 53%

ECG Signals for Biometric Applications - Are we there yet?

Carreiras

Lourenço

Fred

et al. 2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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Abstract:The potential of the electrocardiographic (ECG) signal as a biometric trait has been ascertained in the literature over the past decade. The inherent characteristics of the ECG make it an interesting biometric modality, given its universality, intrinsic aliveness detection, continuous availability, and inbuilt hidden nature. These properties enable the development of novel applications, where non-intrusive and continuous authentication are critical factors. Examples include, among others, electronic trading platforms, the gaming industry, and the auto industry, in particular for car sharing programs and fleet management solutions. However, there are still some challenges to overcome in order to make the ECG a widely accepted biometric. In particular, the questions of uniqueness (inter-subject variability) and permanence over time (intra-subject variability) are still largely unanswered. In this paper we focus on the uniqueness question, presenting a preliminary study of our biometric recognition system, testing it on a database encompassing 618 subjects. We also performed tests with subsets of this population. The results reinforce that the ECG is a viable trait for biometrics, having obtained an Equal Error Rate of 9.01% and an Error of Identification of 15.64% for the entire test population.

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“…Networked computing [8] and experimental analysis [9] have been carried out for health monitoring using WSNs using Bluetooth and ZigBee. Besides health monitoring, autodiagnosis of diseases like cardiovascular diseases [10][11][12] and inflammation [13] have gained more concern. Machine learning algorithms can be used to train with patients' databases.…”

Section: Applications and Case Studies Of State-of-the-art Wirementioning

confidence: 99%