Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers

El_Rahman, Sahar A.

doi:10.1007/s00500-020-04700-6

Cited by 24 publications

(17 citation statements)

References 60 publications

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“…It is then time to train the algorithm using the training set or k-fold cross-validation method. Performance evaluation with the test set follows, measured using such elements as the confusion matrix ( 209 ), and derived metrics such as F1 score, accuracy ( 208 ), and receiver operation characteristic (ROC) curve ( 210 ). Although these metrics are used widely, most have drawbacks ( 13 ).…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

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“…However, the fusion of multiple biometrics modalities in the socalled multimodal biometric systems provides additional information for reliable human identification. (b) Universality: Universality as a main characteristic of a biometric modality, may not be truly universal [349], [390]. For example, the national institute of standards and technology finds that as a result of disabilities, cuts, and bruises, approximately 2% of the world population may not entirely be fingerprinted [393].…”

Section: ) Advantages Of Ecg Signal In Multimodal Biometric Systemsmentioning

confidence: 99%

“…(d) Security: While unimodal biometrics are extremely difficult to be stolen, it is yet feasible to bypass a biometric system employing spoofed traits. Hence, the ECG signals in a multimodal biometric system realize the fusion of decisions exerted beneath distinct traits to increase the security of biometric authentication [349], [390], [391]. That is, if one of the modalities is compromised, the system can still guarantee security employing the remaining biometric modalities since it will be difficult for the intruder to spoof multiple biometric traits.…”

Section: ) Advantages Of Ecg Signal In Multimodal Biometric Systemsmentioning

confidence: 99%

“…Parallel mode is beneficial when all individual biometric recognition systems possess different confusion matrices, but at a drawback of separate sensors required for each modality, even if different modalities can use the same sensor equipment [389]. A multimodal biometric system based on different fusion levels of ECG and fingerprint using different classifiers is proposed in [390], by using 47 human subjects from the MIT-BIH database. Results indicate (area under the ROC curve) up to 0.985 for sequential multimodal system, and up to 0.956 for parallel multimodal system, compared to the unimodal systems that achieved AUC up to 0.951, and 0.866, for the ECG and fingerprint biometrics, respectively.…”

Section: ) Integration Modesmentioning

confidence: 99%

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A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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“…e proposed system can be deployed in real applications according to their advantages. e performance of different classification methods and fusion rules was studied by El_Rahman [17] in the context of multimodal and unimodal biometric systems utilizing the MIT-BIH for electrocardiogram (ECG) database and FVC2004 for fingerprint databases with 47 subjects from virtual multimodal database. e performance of the proposed unimodal and multimodal systems is measured using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC).…”

Section: Introductionmentioning

confidence: 99%

Feature-Level vs. Score-Level Fusion in the Human Identification System

Rasool

2021

Applied Computational Intelligence and Soft Computing

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The design of a robust human identification system is in high demand in most modern applications such as internet banking and security, where the multifeature biometric system, also called feature fusion biometric system, is one of the common solutions that increases the system reliability and improves recognition accuracy. This paper implements a comprehensive comparison between two fusion methods, named the feature-level fusion and score-level fusion, to determine which method highly improves the overall system performance. The comparison takes into consideration the image quality for the six combination datasets as well as the type of the applied feature extraction method. The four feature extraction methods, local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), principle component analysis (PCA), and Fourier descriptors (FDs), are applied separately to generate the face-iris machine vector dataset. The experimental results highlighted that the recognition accuracy has been significantly improved when the texture descriptor method, such as LBP, or the statistical method, such as PCA, is utilized with the score-level rather than feature-level fusion for all combination datasets. The maximum recognition accuracy is obtained at 97.53% with LBP and score-level fusion where the Euclidean distance (ED) is considered to measure the maximum accuracy rate at the minimum equal error rate (EER) value.

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Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers

Cited by 24 publications

References 60 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Feature-Level vs. Score-Level Fusion in the Human Identification System

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