Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

Lim, Ching Leng Peter; Woo, Wai Lok; Dlay, S.S.; Gao, Bin

doi:10.1109/tii.2018.2874462

“…The empirical analysis has shown a reduction in execution time as the solution only uses inter-pulse intervals and an accuracy of 99.3% over 89 individuals. Another technique [55] starts by applying Discrete Waveform Transform (DWT) on ECG signals using heartbeat time intervals and extracts several features to represent the different cardiac states (resting, exercising and recovering) of an individual efficiently. The features are classified using Gaussian-mixture-model (GMM) and Hidden-Markov-Model (HMM) algorithms with user-specific thresholds.…”

Section: Heart-based Biometricsmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

Khan

¹

,

Parkinson

²

,

Grant

³

et al. 2020

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Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.

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“…Some works have used sparse representation based methods for feature extraction and support vector machine (SVM) for the identification [12], [18]. A novel hybrid GMM-HMM based generative model has been developed by Lim et al [19] for person identification. However, the method requires multiple fiducial point detection, which limits its applicability in the practical scenario.…”

Section: Related Workmentioning

confidence: 99%

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

Jyotishi¹,

Dandapat²

2021

Preprint

1

0

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The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person. The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

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“…However, their method needs R-peak detection and beat segmentation. Except for a few works [3], [19], [21], most of the existing methods have not utilised the temporal variation of the time varying ECG signal.…”

Section: Related Workmentioning

confidence: 99%

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

Jyotishi¹,

Dandapat²

2021

Preprint

0

View full text Add to dashboard Cite

The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person. The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

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Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

Cited by 40 publications

References 34 publications

Biometric Systems Utilising Health Data from Wearable Devices

Biometric Systems Utilising Health Data from Wearable Devices

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

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