  In recent years, the popularity of fingerprint-based biometric authentication
systems significantly increased. However, together with many advantages,
biometric systems are still vulnerable to presentation attacks (PAs). In
particular, this applies for unsupervised applications, where new attacks
unknown to the system operator may occur. Therefore, presentation attack
detection (PAD) methods are used to determine whether samples stem from a bona
fide subject or from a presentation attack instrument (PAI). In this context,
most works are dedicated to solve PAD as a two-class classification problem,
which includes training a model on both bona fide and PA samples. In spite of
the good detection rates reported, these methods still face difficulties
detecting PAIs from unknown materials. To address this issue, we propose a new
PAD technique based on autoencoders (AEs) trained only on bona fide samples
(i.e. one-class), which are captured in the short wave infrared domain. On the
experimental evaluation over a database of 19,711 bona fide and 4,339 PA images
including 45 different PAI species, a detection equal error rate (D-EER) of
2.00% was achieved. Additionally, our best performing AE model is compared to
further one-class classifiers (support vector machine, Gaussian mixture model).
The results show the effectiveness of the AE model as it significantly
outperforms the previously proposed methods.
