Development of Heartbeat Detection Kit for Biometric Authentication System

Ramli, Dzati Athiar; Hooi, M.Y.; Chee, Kai Jye

doi:10.1016/j.procs.2016.08.143

Cited by 21 publications

(15 citation statements)

References 7 publications

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“…This study employed Random Forest technique as classifier. Consequently, the prototype of ECG sensor was developed by Ramli et al 12 in order to collect the ECG data in real-time condition. This study implemented wavelet transform as feature extraction by employing Symlet, Daubechies and Coiflet to evaluate the performance of the developed sensor.…”

Section: Related Workmentioning

confidence: 99%

Development of Heartbeat Based Biometric System Using Wavelet Transform

Chin¹,

Ramli²

2018

JES

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Electrocardiogram (ECG) is a new biometric trait that has an advantage as it is an internal modality which indicates to be difficult of counterfeiting. The ECG records the heart activity and it is unique as different individuals have distinct heart structure. Hence, it provides vital information for differentiating one individual from another. One of the techniques to extract the salient information from the ECG signal is by using discrete wavelet transform (DWT). However, in order to develop a reliable ECG based biometric system using DWT, there are many parameters that need to be determined. In this study, we study the effect of different parameters used for de-noising orders, threshold levels and type of mother wavelets to the final authentication performance of our developed system. For the authentication stage, Support Vector Machine (SVM) is used as classifier. The system performance is evaluated based on Genuine Acceptance Rate (GAR), False Acceptance Rate (FAR) and Equal Error Rate (EER) by using the scores given by the SVM. The combination of de-noising order 3, heursure threshold method and db6 mother wavelet yields the top performance with GAR of 92.5% at FAR of 5% and EER of 6.9499% have been achieved. At the end of this paper, a simple ECG based security system is then constructed so as to show the feasibility of this study to be implemented in real-time situation.

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Section: Related Workmentioning

confidence: 99%

Development of Heartbeat Based Biometric System Using Wavelet Transform

Chin¹,

Ramli²

2018

JES

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“…Ramli, Hooi et al (2016) [9], proposed that in their study they developed a portable ECG detection kit for the heartbeat data capturing, and it is a wearable bracelet heartbeat detection device for personal use. Hence, they used the wavelet transform algorithm as a software feature of the device for extraction technique to apply the ECG P-wave, QRS Complex and T-wave features with classification of Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

“…Based on related studies, many methods have been used for pre-processing, in order to remove noises. One of the method for pre-processing is Wavelet de-noised process that suppress noise and observe signal pick clearly [9]. Another method of pre-processing for some noise such as power-line interface, baseline wanders and patient-electrode motion artifacts is band-pass Butterworth filter.…”

Section: Pre-processingmentioning

confidence: 99%

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A Review Study of Heartbeat Biometric Authentication

Alariki¹

2018

JCP

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Today, life engages technology in multiple ways, thus authentication in human technologies is very important. Secure and reliable authentication is in high demand. However, traditional methods for authentication such as passwords and tokens are now outdated because it is possible to steal, lose and share such authentication methods. Current research shows that one of the best methods for authenticating human beings is biometrics. In this paper, the heartbeat biometric, also called Electrocardiographic (ECG), is proposed. The heartbeat biometric is chosen because unique human ECGs cannot be falsely created or replicated. While other biometric methods, such as face recognition, can be compromised by user photographs, or fingerprints, which can be compromised by use of fake fingers, the ECG signal is based on the individualized mechanical movements of each human heart, which features contain unique physiological information. The purpose of this paper is, then, to review various relevant, recent works that study the heartbeat biometric to find the best biometrics features, given the extractions and classification algorithms for the heartbeat biometric signal. This paper concludes that the morphological (P wave) feature is recommended as the most important feature and the Neural Network (NN) classifier is the most reliable classification with the highest performance accuracy for heartbeat biometric. Therefore, to achieve highest accuracy and result for authenticating through heartbeat biometric, it is recommended to consider the mentioned feature extractions and classification.

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“…One of the most essential properties of this signal is the Heart Rate Variability (HRV). Although research in the past mostly focused on the connection between HRV and different types of health disorders [7], the validity of using HRV for biometric recognition is supported by the fact that the physiological and geometrical differences of the heart in different individuals display certain uniqueness in their HRV features [8].…”

Section: Introductionmentioning

confidence: 99%

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

et al. 2020

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The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.

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Development of Heartbeat Detection Kit for Biometric Authentication System

Cited by 21 publications

References 7 publications

Development of Heartbeat Based Biometric System Using Wavelet Transform

Development of Heartbeat Based Biometric System Using Wavelet Transform

A Review Study of Heartbeat Biometric Authentication

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

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