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Biometric presentation attack detection is gaining increasing attention.Users of mobile devices find it more convenient to unlock their smartapplications with finger, face or iris recognition instead of passwords. Inthis paper, we survey the approaches presented in the recent literature todetect face and iris presentation attacks. Specifically, we investigate theeffectiveness of fine tuning very deep convolutional neural networks to thetask of face and iris antispoofing. We compare two different fine tuningapproaches on six publicly available benchmark datasets. Results show theeffectiveness of these deep models in learning discriminative features that cantell apart real from fake biometric images with very low error rate.Cross-dataset evaluation on face PAD showed better generalization than state ofthe art. We also performed cross-dataset testing on iris PAD datasets in termsof equal error rate which was not reported in literature before. Additionally,we propose the use of a single deep network trained to detect both face andiris attacks. We have not noticed accuracy degradation compared to networkstrained for only one biometric separately. Finally, we analyzed the learnedfeatures by the network, in correlation with the image frequency components, tojustify its prediction decision.

Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study