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Automatic fingerprint recognition systems suffer from the threat ofpresentation attacks due to their wide range of applications in areas includingnational borders and commercial applications. Presentation attacks can beperformed by fabricating the fake fingerprint of a user with or without theintention of the subject. This paper presents a dynamic ensemble of deeplearning and handcrafted features to detect presentation attacks inknown-material and unknown-material protocols. The proposed model is a dynamicensemble of deep CNN and handcrafted features empowered deep neural networksboth of which learn their parameters together. The proposed presentation attackdetection model, in this way, utilizes the capabilities of both classificationtechniques and exhibits better performance than their individual results. Theproposed model's performance is validated using benchmark LivDet 2015, 2017,and 2019 databases, with an overall accuracy of 96.10\%, 96.49\%, and 95.99\%attained on them, respectively. The proposed model outperforms state-of-the-artmethods in benchmark protocols of presentation attack detection in terms ofclassification accuracy.

DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection