A Novel Residual-Attention Deep Learning Model for Secure Multimodal Biometric Recognition
Madhumitha Rajendran, Shirley Selvan · Cybernetics & Systems · 2025
In contemporary security applications, unimodal biometric systems are prone to spoofing and environmental variability. To overcome these challenges, MBMRAN (Multibiometric Modified Residual Attention Network), an innovative deep learning framework for multimodal biometric identification, is introduced. MBMRAN leverages 1D convolution-based residual blocks combined with a multi-scale encoder-decoder attention mechanism, enhancing feature extraction and adaptive fusion across four biometric sources: face, iris, palmprint, and fingerprint. Unlike traditional approaches, MBMRAN employs a refined attention mask that preserves critical information while amplifying discriminative cues. Feature-level fusion is optimized through Fisher Vector encoding and Gaussian Mixture Models, enabling robust integration of modality-specific features. Evaluated on the CASIA dataset (305 identities), MBMRAN achieved 94.2% accuracy, surpassing classical models (SVM, KNN, RF) and outperforming several pre-trained deep CNNs. A systematic ablation analysis confirms the essential roles of residual layering, skip connections, and attention scaling. While effective on the CASIA dataset, the model’s depth opens future opportunities for optimization and evaluation across diverse biometric benchmarks. The model also achieved high precision, recall, and F1-scores, validating its generalization and reliability. MBMRAN presents a scalable and computationally efficient solution for identity verification, suited for domains such as access control, smart surveillance, and IoT authentication.