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.
Highlights
N OWADAYS, we encounter biometric recognition systems in many places of our daily life
In order to evaluate the vulnerabilities of biometric systems to presentation attacks (PAs), the following metrics are defined within the ISO/IEC 30107-3 standard on biometric presentation attack detection - part 3: testing and reporting [11]: Attack Presentation Classification Error Rate (APCER): “proportion of attack presentations using the same presentation attack instrument (PAI) species incorrectly classified as bona fide presentations”
One non-generative fingerprint presentation attack detection (PAD) approach has been presented by Ding and Ross [34], who introduced an ensemble of multiple one-class support vector machine (OC-support vector machines (SVMs)) classifiers, each of which is trained on different feature sets
Summary
N OWADAYS, we encounter biometric recognition systems in many places of our daily life. In order to avoid re-training the classifier each time a new PAI species is created, one-class classifiers can be used [8] These models are solely trained on bona fide samples to detect anomalies in unseen data. They are especially designed to generalise much better than multi-class classifiers since all PAs are unknown to them. The evaluation is carried out on data captured in the short wave infrared domain with over 24,000 samples, including 45 different PAI species It should be noted, that the discussed design decisions should be generally applicable for other input data as well.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Biometrics, Behavior, and Identity Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
