The SWAX Benchmark: Attacking Biometric Systems with Wax Figures

TL;DR

This paper introduces the SWAX benchmark dataset, comprising real and wax figure images and videos, to evaluate face spoofing detection methods, revealing ongoing vulnerabilities despite recent advances.

Contribution

It presents the SWAX dataset with diverse face images and videos for face spoofing detection research, highlighting persistent challenges in high-quality spoofing attacks.

Findings

Baseline methods struggle against high-quality spoofing.

The dataset reveals limitations of current face anti-spoofing techniques.

Spoofing attacks remain effective despite recent progress.

Abstract

A face spoofing attack occurs when an intruder attempts to impersonate someone who carries a gainful authentication clearance. It is a trending topic due to the increasing demand for biometric authentication on mobile devices, high-security areas, among others. This work introduces a new database named Sense Wax Attack dataset (SWAX), comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. Experiments performed with baseline methods show that despite the progress in recent years, advanced spoofing methods are still vulnerable to high-quality violation attempts.