Deep Learning-Based Privacy Preserving Multimodal Biometrics Recognition for Cross-Silo Datasets

Abstract

Different biometric modalities, such as fingerprints and left and right eye irises, contain physiological characteristics that offer high accuracy in identification processes. These modalities complement each other; for example, fingerprints provide intricate ridge patterns, while irises exhibit stable, precise features that perform well in challenging environments. A new proposed framework based on federated learning with optimised features, pre-trained deep learning models, linear discriminant analysis and dense neural networks ensures privacy protection for multi-modal biometric recognition across diverse biometric datasets. The system obtains better accuracy levels alongside increased robustness through the combination of fingerprint and iris scan technology that functions across independent and identically distributed (IID) and non-independent and non-identically distributed (non-IID) conditions. Privacy protection functions as a key asset of federated learning because it allows distributed training operations through non-raw data sharing, supporting high classification results. The system's performance is enhanced by implementing feature fusion alongside dimensionality reduction methods, which enhance both the efficiency and resistance to noise and variabilities. The system establishes an essential reference point for distributed and heterogeneous real-world biometric recognition because it implements accurate computation with enhanced efficiency together with privacy protection. The IID data experiments demonstrated 98.86% training accuracy while achieving precision and recall at precise levels of 98.86% and 96.59%. All metrics achieved 100% on the validation data set while keeping loss at zero. The system's performance slightly decreased under non-IID training data conditions, which resulted in 95.01% training accuracy and 0.18 training loss. The reported precision levels matched recall values since both measurements reached 97.99% and 95.01%. The system maintained perfect validation results through all metrics, which demonstrated a strong ability to generalise beyond data distribution impediments. The integration of multimodal biometric systems with federated learning enables the optimisation of large-scale solutions because it establishes efficient but accurate and secure applications across domains that include surveillance and security together with healthcare.