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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ibrahim Omara
Buraydah Private Colleges
Computer Science & IT Engineering

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Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features Mohamed S. Khalaf; Said Fathy Al-Zoghdy; Mariana Barsoum; Ibrahim Omara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3252-3268

Abstract

Biometric authentication systems are essential for secure access control; however unimodal systems are vulnerable to spoofing and environmental variations. This study proposes a dual-modal biometric system combining electrocardiogram (ECG) signals and ear features to enhance security and accuracy. Deep learning architectures including VGG-verydeep16 and convolutional neural network 5 (CNN5) are evaluated for feature extraction and fusion. Experimental results show that hybrid model (VGG-verydeep16 + CNN5) achieves around 98% accuracy, outperforming individual models. The system adapts to dataset size, using CNN5 for large datasets and VGG-verydeep16 for smaller ones. This approach offers a robust, efficient, and scalable solution for real-world biometric authentication.
Co-Authors Mariana Barsoum Mohamed S. Khalaf Said Fathy Al-Zoghdy