Fingerprint Presentation Attack Detection: Generalization and Efficiency
This work addresses security vulnerabilities in biometric systems by enhancing PAD generalization and efficiency for real-world smartphone deployment, though it is incremental as it builds on an existing method.
The paper tackles the problem of fingerprint presentation attack detection (PAD) by identifying a representative set of six PA materials to improve generalization to unseen attacks, and it develops an optimized Android app that maintains high detection performance (TDR from 95.7% to 95.3% at FDR=0.2%) with fast prediction times under 300ms.
We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training. A dataset of 5,743 bonafide and 4,912 PA images of 12 different materials is used to evaluate a state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE visualization and clustering of material characteristics to identify a representative set of PA materials that cover most of PA feature space. We observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh, Gelatin, Latex Body Paint and Monster Liquid Latex provide a good representative set that should be included in training to achieve generalization of PAD. We also propose an optimized Android app of Fingerprint Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4) without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR = 0.2%) which can make a PA prediction in less than 300ms.