This paper describes an iris verification project focused on design and performance evaluation under both matched and mismatched training and testing conditions. Training is always performed on clean iris images. Testing is performed on both clean and noisy iris images. This project is part of a senior undergraduate course on biometric systems. In implementing an iris recognition system, students go through each step, namely, preprocessing, feature extraction, classification (training and use in rendering a decision) and performance evaluation. The Chinese Academy of Sciences - Institute of Automation(CASIA) eye image database known as the CASIA-Iris-Interval-v3 database is used to show students that robustness to mismatched training and testing conditions is a significant practical issue.