In-vehicle driver recognition based on hand ECG signals

Silva, Hugo; Lourenço, André; Fred, Ana

doi:10.1145/2166966.2166971

Cited by 36 publications

(27 citation statements)

References 5 publications

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“…Our system can be further optimized de- pending on the application scenarios. In our previous paper (Silva et al, 2012), a variation of the proposed approach was used to evaluate the applicability of our method to in-vehicle driver recognition. An important aspect within ECG biometric systems, which has been marginally covered in the state-of-the-art is the stability of the signals over time.…”

Section: Discussionmentioning

confidence: 99%

ECG Biometrics: Principles and Applications

2013

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Electrocardiographic (ECG) signals have several properties that can greatly complement the existing, and more established biometric modalities. Some of the most prominent properties are the fact that the signals can be continuously acquired using minimally intrusive setups, are not prone to produce latent patterns, and provide intrinsic liveliness detection, opening new opportunities within the area of biometric systems development. The potential impact of this technique extends to a broad variety of application domains, ranging from the entertainment industry, to digital transactions. In this paper, we present a framework for ECG biometrics, with focus on some of the latest developments and future trends in the field, covering multiple aspects of the problem with the aim of a real-world deployment. Our results so far, further reinforce the feasibility and interest of the method in a multibiometrics approach.

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Section: Discussionmentioning

confidence: 99%

ECG Biometrics: Principles and Applications

2013

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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“…ECG data could be collected from the user's smart watch or activity tracker and compared with an individually stored pattern [143,144]. The main benefit of using this factor for authentication is that ECG signals emerge as a potential biometric modality with the advantage of being difficult (or close to impossible) to mimic.…”

Section: Electrocardiographic (Ecg) Recognitionmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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“…In [12], a study on the biometric performance of full vs. partial waveform is presented. A total of 32 records from the CYHBi short-term dataset were used, to evaluate how the use of the waveform comprehending the P-QRS-T segments compares to using only the R-ST portion of the waveform.…”

Section: Studies With Cybhimentioning

confidence: 99%

“…Nonetheless, this approach is highly intrusive for the subjects, thus limiting the potential industrial applications of ECG-based biometrics. More recently, researchers have been pivoting towards the off-the-person approaches [3,6,[10][11][12][13], in an effort to overcome the intrusiveness problems widely criticized in ECG biometrics. Recent work from our team has provided experimental evidence that the signals obtained through the off-the-person approach using dry electrodes, present virtually the same morphology as those obtained at the Lead I derivation of a standard 12-lead medical-grade ECG system [8,14].…”

Section: Introductionmentioning

confidence: 99%

Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics

Silva

Lourenço

Fred

et al. 2014

Computer Methods and Programs in Biomedicine

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c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) [503][504][505][506][507][508][509][510][511][512][513][514] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources.In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.

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In-vehicle driver recognition based on hand ECG signals

Cited by 36 publications

References 5 publications

ECG Biometrics: Principles and Applications

ECG Biometrics: Principles and Applications

Multi-Factor Authentication: A Survey

Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics

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