Biometric Fusion for Enhanced Authentication in Cloud Computing Environments - Chiyo Miyazawa; Ryosuke Sato (Advances in Engineering and Intelligence Systems)
In the realm of cloud computing, ensuring robust data security is of utmost importance. Authentication, a cornerstone of safeguarding information, continuously evolves to counter escalating threats. This paper introduces MultiFusionGuard, an innovative multimodal biometric authentication framework aimed at bolstering cloud security. By leveraging diverse biometric traits like fingerprints, iris scans, and palm prints, MultiFusionGuard utilizes their unique patterns to enhance security measures. Each trait undergoes meticulous image processing stages, encompassing pre-processing, normalization, and feature extraction. The fusion of these distinct biometric features at multiple levels results in a robust authentication framework. Integration of these features establishes a comprehensive and intricate authentication system, thereby amplifying the complexity and effectiveness of security measures. The system's efficacy is evaluated using indicators such as incorrect rejection ratio, incorrect acceptance ratio, and execution time to ensure reliability and resilience against illegal access attempts. MultiFusionGuard offers a promising solution to fortify data protection within cloud environments, providing advanced defense mechanisms against potential security breaches.