  Diversity and unpredictability of artifacts potentially presented to an iris
sensor calls for presentation attack detection methods that are agnostic to
specificity of presentation attack instruments. This paper proposes a method
that combines two-dimensional and three-dimensional properties of the observed
iris to address the problem of spoof detection in case when some properties of
artifacts are unknown. The 2D (textural) iris features are extracted by a
state-of-the-art method employing Binary Statistical Image Features (BSIF) and
an ensemble of classifiers is used to deliver 2D modality-related decision. The
3D (shape) iris features are reconstructed by a photometric stereo method from
only two images captured under near-infrared illumination placed at two
different angles, as in many current commercial iris recognition sensors. The
map of normal vectors is used to assess the convexity of the observed iris
surface. The combination of these two approaches has been applied to detect
whether a subject is wearing a textured contact lens to disguise their
identity. Extensive experiments with NDCLD'15 dataset, and a newly collected
NDIris3D dataset show that the proposed method is highly robust under various
open-set testing scenarios, and that it outperforms all available open-source
iris PAD methods tested in identical scenarios. The source code and the newly
prepared benchmark are made available along with this paper.
