  Automatic fingerprint recognition systems are the most extensively used
systems for person authentication although they are vulnerable to Presentation
attacks. Artificial artifacts created with the help of various materials are
used to deceive these systems causing a threat to the security of
fingerprint-based applications. This paper proposes a novel end-to-end model to
detect fingerprint Presentation attacks. The proposed model incorporates
MobileNet as a feature extractor and a Support Vector Classifier as a
classifier to detect presentation attacks in cross-material and cross-sensor
paradigms. The feature extractor's parameters are learned with the loss
generated by the support vector classifier. The proposed model eliminates the
need for intermediary data preparation procedures, unlike other static hybrid
architectures. The performance of the proposed model has been validated on
benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall
accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% is achieved on these
databases, respectively. The performance of the proposed model is compared with
state-of-the-art methods and the proposed method outperforms in cross-material
and cross-sensor paradigms in terms of average classification error.
