AI Driven Multi Modal Authentication Using Hand Motion Pins and Iris Biometric Using CNN

In modern digital environments, ensuring secure user authentication has become a critical challenge due to increasing cyber threats and identity fraud. Traditional authentication techniques such as passwords and PINs are vulnerable to attacks including hacking, shoulder surfing, and brute-force methods. This paper proposes an AI-driven multimodal authentication system that integrates iris recognition and dynamic hand-motion PIN authentication using Convolutional Neural Networks (CNNs). The system combines physiological biometric features (iris patterns) with behavioral authentication (hand motion gestures) to enhance security and reliability. CNN models are used for feature extraction and classification, enabling accurate recognition of users. The proposed system provides improved security, reduced false acceptance rates, and stronger resistance against spoofing attacks, making it suitable for secure access control applications.