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

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 convo...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2019-06, Vol.14 (6), p.1419-1431
Main Authors: Kuehlkamp, Andrey, Pinto, Allan, Rocha, Anderson, Bowyer, Kevin W., Czajka, Adam
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biometric recognition systems
Contact lenses
Feature extraction
Forestry
IEC Standards
Image classification
Image detection
Iris recognition
ISO Standards
Lenses
LivDet
presentation attack detection
Printouts
spoofing
State of the art
Task analysis
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Summary: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 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-Iris 2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2018.2878542