Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection

TL;DR

This paper introduces a one-class autoencoder-based method for fingerprint presentation attack detection that effectively identifies unknown attack types with a low error rate, enhancing biometric security.

Contribution

The study proposes a novel one-class autoencoder approach trained solely on genuine fingerprint images for improved detection of unknown presentation attacks.

Findings

Achieved a 2.00% detection equal error rate on a large dataset.

Outperformed other one-class classifiers like SVM and GMM.

Effective detection of diverse attack materials in fingerprint biometrics.

Abstract

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.