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

arXiv: https://arxiv.org/abs/2308.10015 · HTML: https://mlsanity.com/paper/2308.10015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [LivDet in Action - Fingerprint Liveness Detection Competition 2019](https://mlsanity.com/paper/1905.00639/) (2019)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset](https://mlsanity.com/paper/2006.07498/) (2020)
- [Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns](https://mlsanity.com/paper/1508.00537/) (2015)
- [End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module](https://mlsanity.com/paper/1803.07830/) (2018)
- [Fingerprint liveness detection using local quality features](https://mlsanity.com/paper/1806.02974/) (2018)
- [An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network](https://mlsanity.com/paper/2306.03577/) (2023)

## Abstract (arXiv)

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.