Multimodal biometric authentication: a novel deep learning framework integrating ECG, fingerprint, and finger knuckle print for high-security applications

Abstract

Abstract Multimodal biometric systems represent a significant advancement in biometric authentication technology by integrating multiple modalities to enhance accuracy and security. Our proposed system combines electrocardiogram (ECG), fingerprint, and finger knuckle print (FKP) modalities to achieve improved authentication performance, especially suited for high-security applications. The system first uses ECG for liveness detection, ensuring that only genuine users proceed, followed by fingerprint and FKP for authentication. Three individual Siamese Neural Networks were developed, each optimized to extract distinct features from each modality. Data quality was enhanced using preprocessing methods such as noise reduction and normalization, along with data augmentation strategies to improve model robustness. The system was evaluated using a balanced dataset of 50 samples per biometric modality. Individual average accuracies reached 99.54% for ECG, and 100% for both fingerprint and FKP considering 90 subjects. Using weighted average score-level fusion with a priority on fingerprint and FKP, the system achieved an overall accuracy of 99.80%, with a False Acceptance Rate (FAR) of 0.20%, False Rejection Rate (FRR) of 0.21%, Equal Error Rate (EER) of 0.20%, and an F1-Score of 99.80%. These results demonstrate the system's resilience to spoofing and robustness against data variability, offering a highly secure authentication solution with practical applicability in cloud-based high-security environments, such as banking and healthcare.