  The adoption of large-scale iris recognition systems around the world has
brought to light the importance of detecting presentation attack images
(textured contact lenses and printouts). This work presents a new approach in
iris Presentation Attack Detection (PAD), by exploring combinations of
Convolutional Neural Networks (CNNs) and transformed input spaces through
binarized statistical image features (BSIF). Our method combines lightweight
CNNs to classify multiple BSIF views of the input image. Following explorations
on complementary input spaces leading to more discriminative features to detect
presentation attacks, we also propose an algorithm to select the best (and most
discriminative) predictors for the task at hand.An ensemble of predictors makes
use of their expected individual performances to aggregate their results into a
final prediction. Results show that this technique improves on the current
state of the art in iris PAD, outperforming the winner of LivDet-Iris2017
competition both for intra- and cross-dataset scenarios, and illustrating the
very difficult nature of the cross-dataset scenario.
