Topic: Fingerprint Presentation Attack Detection based on local features encoding for Unknown Attacks
by Lazaro Janier Gonzalez-Soler
D19/2.03a, November 14, 2019 (Thursday), 12.00 noon
Keywords — Presentation Attack Detection, local features encoding, probabilistic visual vocabulary
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, in this presentation, two new PAD approaches, which are based on three image representation, are proposed. By transforming these representations into a embedded feature space, we can correctly discriminate bona fide from attack presentations in the aforementioned scenarios.