Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks

• Published in: IEEE access Vol. 9; pp. 5806 - 5820
• Main Authors: Gonzalez-Soler, Lazaro Janier, Gomez-Barrero, Marta, Chang, Leonardo, Perez-Suarez, Airel, Busch, Christoph
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biometrics (access control); Encoding; Error analysis; Feature extraction; fingerprint; Fingerprint recognition; Fingerprints; Local feature encoding; presentation attack detection; probabilistic visual vocabulary; Representations; Training; visual vocabulary
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3048756

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%).