Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
This work addresses security vulnerabilities in iris recognition systems, offering an incremental improvement for biometric authentication.
The paper tackles the problem of detecting presentation attacks in iris recognition systems by proposing an ensemble method combining CNNs and binarized statistical image features, which outperforms the LivDet-Iris2017 winner in both intra- and cross-dataset scenarios.
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.