Face Presentation Attack Detection

TL;DR

This paper discusses face presentation attack detection, highlighting its importance in securing face recognition systems against various spoofing methods like print, replay, and masks, and reviews current detection techniques.

Contribution

The paper provides a comprehensive overview of face PAD methods, emphasizing recent advancements and challenges in detecting diverse spoofing attacks.

Findings

Face PAD is crucial for secure face recognition applications.

Various spoofing methods pose challenges to PAD accuracy.

Recent deep learning approaches improve detection performance.

Abstract

Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios. A presentation attack is first defined in ISO standard as: a presentation to the biometric data capture subsystem with the goal of interfering with the operation of the biometric system. Specifically, PAs range from simple 2D print, replay and more sophisticated 3D masks and partial masks. To defend the face recognition systems against PAs, both academia and industry have paid extensive attention to developing face presentation attack detection (PAD) technology (or namely `face anti-spoofing (FAS)').