COLFISPOOF: A new Database for Contactless Fingerprint Presentation Attack Detection Research
Jascha Kolberg, Jannis Priesnitz, Christian Rathgeb, Christoph Busch
Abstract
Contactless fingerprint recognition is known for its high user comfort and low hygienic concerns. However, contact-less fingerprint recognition, especially in mobile and un-supervised scenarios, is vulnerable to presentation attacks. Presentation Attack Detection (PAD) in biometric systems like contactless fingerprint recognition is more challenging compared with contact-based modalities because many de-tection mechanisms rely on direct contact between the finger and the surface of the capture device. Hence, in contactless scenarios it is generally possible to present more Presentation Attack Instruments (PAIs) like printout or replay arte-facts. In this work, we introduce COLFISPOOF, a new database for contactless fingerprint PAD. The database is acquired using a contactless fingerprint recognition system utilizing a smartphone as capturing device. It comprises 7,200 samples of 72 different PAI species and was captured with two different smartphone models. The database is publicly available for research purposes such that interested researchers can download and use it to develop new PAD algorithms. Moreover, we define evaluation protocols for training and testing of machine learning algorithms such that future PAD algorithms can be benchmarked on this database in a comparable and reproducible way.