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%).INDEX TERMS Local feature encoding, presentation attack detection, fingerprint, probabilistic visual vocabulary, visual vocabulary.

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In spite of the advantages of using fingerprints for subject authentication, several works have shown that fingerprint recognition systems can be easily circumvented by means of artificial fingerprints or presentation attack instruments (PAIs). In order to address that threat, the existing presentation attack detection (PAD) methods have reported a high detection performance when materials used for the fabrication of PAIs and capture devices are known. However, for more complex and realistic scenarios where one of those factors remains unknown, these PAD methods are unable to correctly separate a PAI from a real fingerprint (i.e. bona fide presentation). In this article, a new PAD approach based on the Fisher Vector technique, which combines local and global information of several local feature descriptors in order to improve the PAD generalisation capabilities, was proposed. The experimental results over unknown scenarios taken from LivDet 2011 to LivDet 2017 show that our proposal reduces the top state-of-the-art average classification error rates by up to four times, thereby making it suitable in real applications demanding high security. In addition, the best single configuration achieved the best results in the LivDet 2019 competition, with an overall accuracy of 96.17%.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Fingerprint-based biometric systems have experienced a large development in the last years. Despite their many advantages, they are still vulnerable to presentation attacks (PAs). 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 issue which has received a lot of attention recently. Nowadays, when the materials for the fabrication of the Presentation Attack Instruments (PAIs) have been used to train the PA Detection (PAD) methods, the PAIs can be successfully identified. However, current PAD methods still face difficulties detecting PAIs built from unknown materials or captured using other sensors. Based on that fact, 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 2015 databases, yielded error rates outperforming the top state-of-the-art results by up to 50% in the most challenging scenarios. In addition, the best configuration achieved the best results in the LivDet 2019 competition (overall accuracy of 96.17%).