Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features

Mohamed S. Khalaf (Menoufia University)
Said Fathy Al-Zoghdy (Menoufia University)
Mariana Barsoum (Menoufia University)
Ibrahim Omara (Buraydah Private Colleges)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 4: August 2026

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.