  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 Python and uses only open source resources, such
as OpenCV. This method does not incorporate iris image segmentation, which may
be problematic for unknown fake samples. Instead, it makes a best guess to
localize the rough position of the iris. The PAD-related features are extracted
with the Binary Statistical Image Features (BSIF), which are classified by an
ensemble of classifiers incorporating support vector machine, random forest and
multilayer perceptron. The models attached to the current software have been
trained with the NDCLD'15 database and evaluated on the independent datasets
included in the LivDet-Iris 2017 competition. The software implements the
functionality of retraining the classifiers with any database of authentic and
attack images. The accuracy of the current version offered with this paper
exceeds 99% when tested on subject-disjoint subsets of NDCLD'15, and oscillates
around 85% when tested on the LivDet-Iris 2017 benchmarks, which is on par with
the results obtained by the LivDet-Iris 2017 winner.
