ECG-Based User Authentication and Identification Method on VANETs

Santos, Alex de Assis Santos dos; Medeiros, Iago; Resque, Paulo; Rosário, Denis; Nogueira, Michele; Santos, Aldri; Cerqueira, Eduardo; Chowdhury, Kaushik R.

doi:10.1145/3277103.3277138

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…The parameters and values evaluated were max_depth (60, 80, 100), min_samples_leaf (3,4,5), min_samples_split (8,10,12) and n_estimators (80, 100, 120). Specifically, the maximum depth of the tree means nodes are expanded until all leaves are pure.…”

Section: Methodsmentioning

confidence: 99%

“…The remaining points are classified as R Peak. The first local maximum values back and ahead to the R peak are classified as points Q and S, respectively [12]. In this way, the algorithm finds the Q, R, and S amplitude for each beat.…”

Section: Feature Extraction and Outlier Removalmentioning

confidence: 99%

“…We consider RF for the classification step since it presents good results without the need for a longer time to fit [12]. RF is a collection of DT trained with randomly selected data, guaranteeing that each tree is slightly different from each other.…”

Section: Classificationmentioning

confidence: 99%

“…In this sense, to validate the ECG as biometric for user authentication, ML models should be trained with a higher number of subjects, simulating the scenario presented in real-world applications. Most of the existing works on ECG for user authentication and identification [7,8,[11][12][13] analyzed datasets with 20 to 290 people records. Such amount of data is not enough to provide user authentication and identification system with scalability, such as the system operation today.…”

Section: Introductionmentioning

confidence: 99%

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Data Improvement Model Based on ECG Biometric for User Authentication and Identification

Barros

Resque

Almeida

et al. 2020

Sensors

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The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Extraction and Outlier Removalmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Improvement Model Based on ECG Biometric for User Authentication and Identification

Barros

Resque

Almeida

et al. 2020

Sensors

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“…It measures the driver’s ECG from the vehicle’s steering wheel and identifies the driver with an error rate of 3 to 5% in a static state and a 30% error rate in a dynamic state. Santos et al [ 46 ] proposed a driver identification system using the stress recognition in automobile driver’s DB provided by Physionet. This system identifies the driver with 95% accuracy by extracting features from the segmented ECG based on the fiducial point.…”

Section: Biometrics Technique Using Ecg Signal For Intelligent Vehmentioning

confidence: 99%

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

Choi

Lim

Pan

2020

Sensors

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Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

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