# Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal

## TL;DR

This paper introduces face-GPAD, an open-source framework and dataset for evaluating the generalization ability of face presentation attack detection methods under various real-world conditions.

## Contribution

It provides a new evaluation framework, protocols, and a large dataset to assess face PAD methods' robustness and generalization in diverse scenarios.

## Key findings

- New open-source evaluation framework for face PAD
- Protocols for assessing resolution and adversarial condition effects
- A large, categorized dataset for PAD research

## Abstract

Over the past few years, Presentation Attack Detection (PAD) has become a fundamental part of facial recognition systems. Although much effort has been devoted to anti-spoofing research, generalization in real scenarios remains a challenge. In this paper we present a new open-source evaluation framework to study the generalization capacity of face PAD methods, coined here as face-GPAD. This framework facilitates the creation of new protocols focused on the generalization problem establishing fair procedures of evaluation and comparison between PAD solutions. We also introduce a large aggregated and categorized dataset to address the problem of incompatibility between publicly available datasets. Finally, we propose a benchmark adding two novel evaluation protocols: one for measuring the effect introduced by the variations in face resolution, and the second for evaluating the influence of adversarial operating conditions.