Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database

TL;DR

This paper introduces Flickr-PAD, a new high-resolution face presentation attack detection database derived from open-access Flickr images, enabling more realistic evaluation of PAD algorithms in diverse scenarios.

Contribution

The paper presents Flickr-PAD, a novel high-quality, diverse database for face PAD, addressing limitations of existing low-resolution datasets and facilitating more realistic research evaluations.

Findings

MobileNet-V3 large achieved BPCER10 of 7.08%

EfficientNet-B0 performed comparably in PAD tasks

Flickr-PAD enhances dataset diversity for PAD research

Abstract

Nowadays, Presentation Attack Detection is a very active research area. Several databases are constituted in the state-of-the-art using images extracted from videos. One of the main problems identified is that many databases present a low-quality, small image size and do not represent an operational scenario in a real remote biometric system. Currently, these images are captured from smartphones with high-quality and bigger resolutions. In order to increase the diversity of image quality, this work presents a new PAD database based on open-access Flickr images called: "Flickr-PAD". Our new hand-made database shows high-quality printed and screen scenarios. This will help researchers to compare new approaches to existing algorithms on a wider database. This database will be available for other researchers. A leave-one-out protocol was used to train and evaluate three PAD models based on MobileNet-V3 (small and large) and EfficientNet-B0. The best result was reached with MobileNet-V3 large with BPCER10 of 7.08% and BPCER20 of 11.15%.