This project presents the development of an Inclusive Iris Recognition System for Automated Teller Machines (ATMs) aimed at enhancing both security and accessibility for users. Traditional ATM authentication methods, such as PINs and cards, often pose challenges for individuals with disabilities, particularly those with visual impairments. Our system leverages advanced iris recognition technology, which offers a secure and user-friendly alternative. The project involves designing a hardware setup featuring a high-resolution iris camera integrated with a processing unit, alongside a software framework for image capture, processing, and recognition. By employing algorithms for feature extraction and machine learning techniques, we ensure accurate user identification while maintaining data security through encryption. To promote inclusivity, the system includes accessibility features such as audio prompts and adjustable camera positioning. Rigorous testing with diverse user groups demonstrates the system's effectiveness in accurately recognizing iris patterns and its usability for individuals with varying needs. Overall, this project not only addresses the security challenges faced by ATMs but also contributes to a more inclusive banking experience, paving the way for broader adoption of biometric technologies in everyday applications.
Jadhav et al. (Thu,) studied this question.