DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for
  Fingerprint Presentation Attack Detection
Anuj Rai Parsheel Kumar Tiwari Jyotishna Baishya Ram Prakash Sharma Somnath Dey
Automatic fingerprint recognition systems suffer from the threat of
presentation attacks due to their wide range of applications in areas including
national borders and commercial applications. Presentation attacks can be
performed by fabricating the fake fingerprint of a user with or without the
intention of the subject. This paper presents a dynamic ensemble of deep
learning and handcrafted features to detect presentation attacks in
known-material and unknown-material protocols. The proposed model is a dynamic
ensemble of deep CNN and handcrafted features empowered deep neural networks
both of which learn their parameters together. The proposed presentation attack
detection model, in this way, utilizes the capabilities of both classification
techniques and exhibits better performance than their individual results. The
proposed 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-art
methods in benchmark protocols of presentation attack detection in terms of
classification accuracy.