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

TL;DR

This paper introduces DyFFPAD, a dynamic fusion model combining deep CNN and handcrafted features to improve fingerprint presentation attack detection across various protocols, outperforming existing methods.

Contribution

The paper proposes a novel dynamic ensemble approach that fuses deep CNN and handcrafted features for enhanced fingerprint attack detection.

Findings

Achieved over 94.99% accuracy on benchmark datasets.

Outperforms state-of-the-art methods in classification accuracy.

Validated on multiple benchmark databases from 2015, 2017, and 2019.

Abstract

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.