Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks

TL;DR

This paper introduces a new fingerprint presentation attack detection method that combines local and global image features to effectively identify unknown attacks, outperforming existing techniques on multiple datasets.

Contribution

The proposed technique uniquely integrates local and global fingerprint features in a common space, enhancing detection of unknown presentation attacks.

Findings

Outperforms state-of-the-art methods by up to 50% in challenging scenarios

Achieves 96.17% overall accuracy in LivDet 2019 competition

Effective in detecting attacks from unknown materials and sensors

Abstract

Fingerprint-based biometric systems have experienced a large development in the last years. Despite their many advantages, they are still vulnerable to presentation attacks (PAs). Therefore, the task of determining whether a sample stems from a live subject (i.e., bona fide) or from an artificial replica is a mandatory issue which has received a lot of attention recently. Nowadays, when the materials for the fabrication of the Presentation Attack Instruments (PAIs) have been used to train the PA Detection (PAD) methods, the PAIs can be successfully identified. However, current PAD methods still face difficulties detecting PAIs built from unknown materials or captured using other sensors. Based on that fact, we propose a new PAD technique based on three image representation approaches combining local and global information of the fingerprint. By transforming these representations into a common feature space, we can correctly discriminate bona fide from attack presentations in the aforementioned scenarios. The experimental evaluation of our proposal over the LivDet 2011 to 2015 databases, yielded error rates outperforming the top state-of-the-art results by up to 50\% in the most challenging scenarios. In addition, the best configuration achieved the best results in the LivDet 2019 competition (overall accuracy of 96.17\%).