multi factor authentication

This research highlights the significance of biometric authentication in ensuring secure and reliable identification across various domains. The proposed approach combines fingerprint and eye biometric features, leveraging the "or" relationship to authenticate individuals using either feature when necessary. By incorporating multiple biometric features, the model enhances accuracy and reliability, providing a comprehensive solution for identity verification. The use of a convolutional neural network (CNN) enables high accuracy and effective processing of diverse image types. The results demonstrate remarkable performance, with accuracy rates exceeding 99% in both fingerprint and eye biometric authentication. The model's integration of multiple features and advanced machine learning techniques enhances authentication processes, enabling better management of access control and reducing unauthorized access risks. Future research should explore expanding the range of biometric features and further improving the model's architecture and algorithms to adapt to emerging technologies and security requirements. Overall, this research emphasizes the importance of biometric authentication, presents an effective approach, and paves the way for advancements in the field, enabling organizations to establish robust and secure identification systems.