Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

Andrey Kuehlkamp, Allan Pinto, Anderson Rocha, Kevin Bowyer, Adam Czajka

Abstract:

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 paper 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 (BSIFs). Our
method combines lightweight CNNs to classify multiple BSIF
views of the input image. Following explorations on complemen-
tary 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-Iris
2017 competition both for intra- and cross-dataset scenarios, and
illustrating the very difficult nature of the cross-dataset scenario.