Physics-Inspired Multimodal Biometric Recognition Framework: Integrating Optical Characteristics in Iris and Fingerprint Identification

Abstract

A novel physics-informed multimodal biometric recognition framework that unifies iris and fingerprint modalities through a physically interpretable computational architecture. Conventional unimodal biometric systems are often constrained by intra-class variability, environmental distortions, and spoofing vulnerabilities. To address these, we model fingerprint textures as quasi-static surface deformations influenced by erosion and pressure dynamics, while the iris is treated as a dynamic optical structure modulated by pupil dilation and angular displacements. These physical analogs inform the feature extraction process, which employs the Wavelet Scattering Transform to capture frequency and motion-invariant features across spatial scales. A Siamese Neural Network is trained to perform metric-based classification, discerning identity through an abstract similarity embedding. Quantitative results demonstrate substantial gains: at 150 training epochs, the model achieves 98.4% training, 98.1% validation, and 98.0% test accuracy, with minimal loss. Furthermore, a SHAP based interpretability module yields a Decision Robustness of 95.73% and a Feature Attribution Strength of 91.23%, confirming the model's transparency and consistency. This interdisciplinary approach, rooted in dynamic systems theory, wave-based feature modeling, and interpretable deep learning, offers a promising pathway for resilient and explainable biometric authentication under real-world variabilities.