MACET: A Novel Approach to Secure Multimodal Biometric Authentication with Cancellable Templates

Biometric authentication is a cornerstone of modern security systems, yet concerns regarding privacy and data security persist. Cancellable biometrics offer a solution by transforming raw biometric data into non-invertible representations, ensuring security even in the event of a data breach. This study presents Multimodal Affine Cover-space Euler Transformation (MACET), a novel framework designed to enhance biometric template security while preserving authentication accuracy. The proposed approach is based on the hypothesis that Affine Cover Space transformation combined with Euler’s form can generate irreversible templates for multimodal biometrics, specifically fingerprint and iris data, without compromising recognition performance. The methodology involves feature extraction, inverse matrix computation, affine transformation, and Euler-based augmentation, ensuring robust and secure biometric template generation. Experimental results, conducted on a dataset of 450 biometric samples, demonstrate the effectiveness of MACET in improving authentication performance. The system achieves an Equal Error Rate (EER) of 0.0046 and an Area Under the ROC Curve (AROC) of 0.9886, indicating high accuracy. Additionally, the method significantly reduces storage memory size to 1.37 KB per template while maintaining an average execution time of 10.89 seconds. Robustness analysis against spoofing attacks confirms the system's ability to resist unauthorized access, ensuring strong security and privacy protection. These findings establish MACET as a highly secure, computationally efficient, and privacy-preserving biometric authentication framework, suitable for real-world applications. Future research could extend this approach to additional biometric modalities and large-scale authentication systems.