DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection
This addresses the threat of spoof attacks in automatic fingerprint recognition systems, which are widely deployed in areas like national borders and commercial applications, and is incremental as it combines existing feature types for improved performance.
The paper tackles the problem of fingerprint presentation attack detection by proposing a dynamic ensemble of deep CNN and handcrafted features, achieving overall accuracies of 96.10%, 96.49%, and 94.99% on benchmark databases from the Liveness Detection Competition in 2015, 2017, and 2019, respectively.
Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of deployment in areas including national borders and commercial applications. A presentation attack can be performed by creating a spoof of a user's fingerprint with or without their consent. This paper presents a dynamic ensemble of deep CNN and handcrafted features to detect presentation attacks in known-material and unknown-material protocols of the liveness detection competition. The proposed presentation attack detection model, in this way, utilizes the capabilities of both deep CNN and handcrafted features techniques and exhibits better performance than their individual performances. We have validated our proposed method on benchmark databases from the Liveness Detection Competition in 2015, 2017, and 2019, yielding overall accuracy of 96.10%, 96.49%, and 94.99% on them, respectively. The proposed method outperforms state-of-the-art methods in terms of classification accuracy.