MoSFPAD: An end-to-end Ensemble of MobileNet and Support Vector Classifier for Fingerprint Presentation Attack Detection

Abstract

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.