# Fingerprint Presentation Attack Detection: Generalization and Efficiency

## TL;DR

This paper investigates fingerprint presentation attack detection (PAD) generalization to unseen materials, identifies key materials for training, and develops an efficient Android app for real-time PAD on smartphones.

## Contribution

It introduces a representative set of PA materials for improved PAD generalization and presents an optimized mobile app that maintains high detection performance with low latency.

## Key findings

- Six PA materials effectively cover most feature space.
- PAD performance remains high on smartphones with minimal drop.
- Real-time PAD achieved in under 300ms on a commodity device.

## Abstract

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.