<![CDATA[In the evolving landscape of information technology, this study presents a bimodal biometric authentication system that combines facial and iris recognition using advanced Convolutional Neural Networks (CNNs) to address the escalating security concerns of personal and sensitive information. The bimodal approach leverages the unique textures of facial and iris features to create a robust and secure authentication mechanism, demonstrating high accuracy (95.76%) and precision value of 97.83%, low False Acceptance Rate (2.54%) and False Rejection Rate (14.29%). The system framework integrates the strengths of both facial and iris modalities, mitigating vulnerabilities inherent in unimodal systems and advancing the field of biometric authentication by providing a resilient solution against emerging cyber threats, enhancing the reliability of user identification, and contributing to safer digital environments across various domains.]]>
Copyrights © 2026
