Abstract
Fingerprint-based biometric systems have experienced a large development in the past. In spite of many advantages, they are still vulnerable to attack presentations (APs). Therefore, the task of determining whether a sample stems from a live subject (i.e., bona fide) or from an artificial replica is a mandatory requirement which has recently received a considerable attention. Nowadays, when the materials for the fabrication of the Presentation Attack Instruments (PAIs) have been used to train the Presentation Attack Detection (PAD) methods, the PAIs can be successfully identified in most cases. However, current PAD methods still face difficulties detecting PAIs built from unknown materials and/or unknown recepies, or acquired using different capture devices. To tackle this issue, we propose a new PAD technique based on three image representation approaches combining local and global information of the fingerprint. By transforming these representations into a common feature space, we can correctly discriminate bona fide from attack presentations in the aforementioned scenarios. The experimental evaluation of our proposal over the LivDet 2011 to 2019 databases, yielded error rates outperforming the top state-of-the-art results by up to 72% in the most challenging scenarios. In addition, the best representation achieved the best results in the LivDet 2019 competition (overall accuracy of 96.17%).
Highlights
Biometric recognition is based on the observation of distinctive anatomical and behavioural characteristics to automatically recognise a subject [1]
The detection performance is evaluated in compliance with the ISO/IEC 30107-3 [5]: we report the Attack Presentation Classification Error Rate (APCER), which refers to the percentage of misclassified presentation attacks for a fixed threshold, and the Bona Fide Presentation Classification Error Rate (BPCER), which indicates the percentage of misclassified bona fide presentations
In order to establish a fair benchmark with the existing literature, we report the Average Classification Error Rate (ACER) as the average of the APCER and the BPCER for a fixed detection threshold δ
Summary
Biometric recognition is based on the observation of distinctive anatomical and behavioural characteristics to automatically recognise a subject [1]. Fingerprints offer a high recognition accuracy and at the same time enjoy a high popular acceptance. Despite of these advantages, fingerprint-based recognition systems can be circumvented by launching Attack Presentations (APs), in which an artificial fingerprint, denoted as Presentation Attack Instrument (PAI), is presented to a capture device [2]–[5]. In 2000 Zwiesele et al [6] reported that they fooled three commercial fingerprint capture devices with PAIs made of india rubber. In 2009, Japan reported the detection of presentation attacks in one of its airports, and in 2013, a Brazilian doctor used artificial silicone fingerprints to tamper a biometric attendance system at the Sao Paulo hospital [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: 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.
