Biometric authentication powered by Artificial Intelligence (AI) has arisen as a vital solution for ensuring secure access to digital healthcare data. By leveraging advanced AI-driven algorithms, such systems can accurately recognize and verify users based on unique biological traits. However, various existing authentication method suffer from limitations such as noise distortion, poor illumination handling, redundant features, weak multimodal fusion and reduced capability in distinguishing between genuine and fake biometric inputs. To address these challenges, physics inspired deep learning based two-step biometric authentication verification framework is developed to enhance healthcare data protection. The system integrates originality verification of iris and fingerprint modalities to achieve high reliability and precision in healthcare user verification. Initially, biometric images like fingerprint and iris are pre-processed using Wavelet-Inspired Invertible Network (WINNet) for Denoising and the Detail-Enhanced Attention Network (DEA-Net) for illumination and contrast enhancement. Textural features are then extracted using the Hexadecimal Local Adaptive Binary Pattern (HLABP) technique. Both finger print and iris features are adaptively combined through the Adaptive Feature Fusion Mechanism (AFFM) to minimize redundancy and improve representational strength. Finally, the Physics-Informed Neural Network based First-Order Reliability Method (PINN-FORM) classifiers performs biometric recognition to differentiate real from fake data. Upon successful verification, a secure QR code is generated through the Stylize aEsthEtic (SEE) mechanism exclusively for legitimate users. The users then scan the QR code for further biometric verification. If the biometric matches, the user can access the data, otherwise the request is declined. The suggested multimodal biometric authentication framework demonstrates exceptional performance, achieving a precision rate of 97.87%, a F1-Score of 97.82%, and an accuracy of 97.77%. This proposed approach significantly improves accuracy and reliability, ensuring stronger digital data security in healthcare systems.
Shailaja et al. (Sun,) studied this question.
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