Notre Dame researchers release open-source iris recognition tools built for NIST testing Researchers at the University of Notre Dame have developed a new open-source toolkit intended to make iris recognition technology more transparent, easier to test, and more accessible to academic researchers working outside the commercial biometric industry. The paper, Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition, presents two new iris recognition algorithms, along with open-source implementations designed to comply with the National Institute of Standards and Technology’s (NIST) Iris Exchange, known as IREX. The work is aimed at a long-standing gap in biometric testing, as NIST’s IREX program has largely evaluated closed-source commercial iris recognition systems rather than open academic tools. The researchers say that matters because iris recognition is increasingly used in security and identity systems, but many of the most capable algorithms remain proprietary, and that limits outside review, makes reproducibility difficult, and leaves researchers without a strong open baseline for comparing new methods. It also creates problems for forensic uses of iris recognition, where explainability and human interpretation can be important. The paper introduces two new neural-network-based methods. The first, called TripletIris, uses a ConvNeXt-tiny model trained with batch-hard triplet loss. In simple terms, the model learns to pull images of the same iris closer together in a mathematical feature space while pushing images of different irises farther apart. The second, called ArcIris, uses a ResNet100 model trained with ArcFace loss, a method designed to create clearer separation between identities. The researchers also created IREX-compliant C++ versions of two existing Notre Dame iris recognition methods. One, HDBIF, uses human saliency-driven filtering to encode iris texture. The other, CRYPTS, detects and compares Fuchs’ crypts, visible structures in the iris that can be useful in human-interpretable forensic analysis. CRYPTS is particularly