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Exploring a Bio-Inspired Algorithm With Feature Fusion Approach for Enhanced ECG-Based Biometric Authentication

Exploring a Bio-Inspired Algorithm With Feature Fusion Approach for Enhanced ECG-Based Biometric Authentication
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Author(s): Aditya Ojha (Banaras Hindu University, Varanasi, India), Sneha Singh (Banaras Hindu University, Varanasi, India)and Jyoti Singh Kirar (Jawaharlal Nehru University, New Delhi, India)
Copyright: 2026
Pages: 50
Source title: Exploring the Intersection of Forensics and Biometrics
Source Author(s)/Editor(s): Sarah Benziane (University of Science and Technology in Oran, Algeria)
DOI: 10.4018/979-8-3373-4972-5.ch004

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Abstract

Biometric systems based on ECG signals have gained significant attention recently for various biomedical applications, including disease diagnosis and patient monitoring. On the other hand, the efficient extraction and selection of discriminative features from ECG signals remain important hurdles in constructing robust and reliable recognition systems. Traditional feature selection approaches suffer from high computing complexity and poor performance. In this study, we explore the application of nature-inspired algorithms named as Grey Wolf Optimization algorithm (GWO), Whale Optimization algorithm (WO), Cuckoo Search algorithm (CS), Elephant Herding Optimization algorithm (EHO), Bat Algorithm (BA) in the field of ECG biometrics. Here we used an ECG-ID database for experimentation. These algorithms aim to optimize the feature selection process by exploring the search space and selecting the most informative features. The results demonstrate the efficacy of these algorithms in improving the accuracy, robustness, and security of ECG biometric systems

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