Open Source Presentation Attack Detection Baseline for Iris Recognition

This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses. This software can serve as a baseline in various PAD evaluations, and also as an open-source platform with an up-to-date reference method for iris PAD. The software is written in C++ and uses only open source resources, such as OpenCV. The method does not require iris image segmentation and uses Binary Statistical Image Features (BSIF) to extract PAD-related features, which are classified by an ensemble of SVM classifiers. The SVM models attached to the current software have been trained with the NDCLD'15 database and the correct recognition rate exceeds 98%. However, the software implements the functionality of retraining the classifiers with any database of authentic and attack images.