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.

Whereas other biometric characteristics, such as the face, are readily available for an eventual attacker through social media or easy to capture with a conventional smartphone, vein patterns can only be acquired with dedicated sensors. This fact makes them relevant not only for recognition purposes but especially for Presentation Attack Detection (PAD), for instance, in combination with fingerprint recognition. In this chapter, we make use of this combination and present a finger vein-based PAD algorithm to detect presentation attacks targeting fingerprint recognition. The experiments are carried out on a newly collected database, comprising 32 species of Presentation Attack Instruments ranging from printed artefacts to more sophisticated fingerprint overlays. The results show that our method preserves a convenient usage while detecting around 90% of the attacks. However, thin and transparent fingerprint overlays remain very challenging.