Continuous authentication by electrocardiogram data

Guennoun, Mouhcine; Abbad, Najoua; Talom, Jonas; Rahman, Sk. Md. Mizanur; El-Khatib, Khalil

doi:10.1109/tic-sth.2009.5444466

Cited by 37 publications

(24 citation statements)

References 6 publications

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“…To the best of our knowledge, in the literature there are only a few studies tackling continuous authentication based on ECG signals [18], [19], [20]. The method presented in [18] performs authentications every 35 heartbeats, whereas in [19], [20] time intervals of 5s are used. These methods, however, have been evaluated on small datasets of signals encompassing a maximum period of 15 minutes.…”

Section: Related Workmentioning

confidence: 99%

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Labati

Piuri

Sassi

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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Abstract-The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.In this work, we propose an approach for unsupervised periodical re-enrollment for continuous authentication, based on ECG signals. During the enrollment phase, a "super" template obtained from a fixed number of samples is stored in the gallery. In continuous authentication, an update condition is periodically verified. If the condition is satisfied, confirming that the fresh data pertain to the stored identity, an update strategy is applied to fuse the fresh data with the "super" template. Different update conditions and update strategies are presented and evaluated.Tests have been performed on a significantly large public dataset of 24h Holter signals acquired in uncontrolled conditions, proving that the proposed approach obtains a relevant accuracy, which increases with respect to traditional biometric approaches based on a single enrolled template for each individual.

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Section: Related Workmentioning

confidence: 99%

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Labati

Piuri

Sassi

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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“…Using Soft Biometrics for Continuous Authentication has been studied using different types of Soft Biometric traits such as color of clothing and skin [2], keystroke dynamics [5] and electrocardiogram data [6]. Of these the most relevant is the work by Niinuma et al where they implemented checking the color of shirt and skin continually to match the template created at the start of the session [2].…”

Section: Related Workmentioning

confidence: 99%

Continuous multimodal user authentication: coupling hard and soft biometrics with support vector machines to attenuate noise

Srinivasa

Gosukonda

2014

CSIT

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Static Authentication provides a secure framework for a one-time authentication session, but fails to authenticate the user throughout the session. This presents the possibility of an imposter gaining access when a user session is active and the user moves away from the system. The goal of continuous authentication is to authenticate the user right from the initial stages of log-in till log-out. Intuitively, this can be implemented by extrapolating the tried-and-tested static authentication techniques throughout the session. However, extrapolating one-time authentication techniques poses new challenges of being computationally expensive, restricting the user's movement and postures in front of the system, depending on extra expensive hardware and deviating the user from his workflow. In these situations, the user no longer remains uninterrupted by the authentication process in the background. The proposed framework provides unobtrusive Continuous Authentication, by alternating between two modes which utilize hard and soft biometrics respectively, depending on certain confidence parameters. We use facial features as the hard biometric trait for recognizing the user. Employing face recognition for extended periods of time produces noise, which is dampened by using a supervised machine learning algorithm. The color of user's clothing as the soft biometric trait relieves the CPU of comparatively high computation and relaxes constraints on the user's upper body movement.

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“…At the best of our knowledge, there are no studies on the use of PPG signals in continuous authentication systems and the only continuous authentication techniques based on onedimensional physiological signals are described in [12,13]. These studies are based on commercial biometric recognition systems.…”

Section: Related Workmentioning

confidence: 99%

“…These studies are based on commercial biometric recognition systems. The method presented in [12] performs authentications based on ECG signals every 35 heartbeats and the one detailed in [13] uses time intervals of 5 s.…”

Section: Related Workmentioning

confidence: 99%

“…In applicative contexts like hospitals and body sensor networks [2], the use of biometric systems based on physiological signals should therefore permit to perform recognitions based on existing sensors, without introducing additional costs. Another important characteristic of the one-dimensional physiological signals is that they can be used in continuous authentication systems [11][12][13] since they can be captured for long time periods without requiring any user's cooperation. Other advantages of these signals are that they can be acquired from all the individuals and can be obtained only from living people [14].…”

Section: Introductionmentioning

confidence: 99%

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A preliminary study on continuous authentication methods for photoplethysmographic biometrics

Bonissi

Labati

Perico

et al. 2013

2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications

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With respect to other physiological signals, PPG data present some advantages since they can be captured using widely diffused, comfortable, and low-cost sensors.In this paper, we present a feasibility study on continuous authentication techniques based on PPG signals. A biometric recognition method based on a correlation approach is first presented. Then, the accuracy of this method is evaluated on different datasets describing signals of variable time duration. Finally, the performance of continuous enrollment strategies are investigated.The obtained results suggest that PPG signals present sufficient discriminability to be used in biometric applications that do not require very high accuracy. Moreover, the use of continuous enrollment strategies can improve the performance of continuous authentication systems.

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Continuous authentication by electrocardiogram data

Cited by 37 publications

References 6 publications

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Continuous multimodal user authentication: coupling hard and soft biometrics with support vector machines to attenuate noise

A preliminary study on continuous authentication methods for photoplethysmographic biometrics

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