# Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier

## TL;DR

This paper introduces a one-class classifier approach for fingerprint spoof detection using GANs trained solely on live fingerprints, enhancing cross-material spoof detection capabilities.

## Contribution

The paper proposes a novel one-class classification method using GANs trained only on live fingerprints to detect spoofs from unseen materials.

## Key findings

- Improved cross-material spoof detection over state-of-the-art methods.
- Effective detection of spoofs made from unseen materials.
- Trained on 11.8K live images and tested on 5.5K spoof images.

## Abstract

Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of "live" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively.