Detection of Makeup Presentation Attacks based on Deep Face Representations

TL;DR

This paper investigates the vulnerability of face recognition systems to makeup-based presentation attacks and introduces a machine learning detection method using deep face representations and synthetic makeup attacks, achieving high accuracy.

Contribution

It presents a novel detection scheme for makeup presentation attacks using deep face features and synthetic data generated by GANs, improving security in face recognition.

Findings

Detection EER of 0.7% for makeup attacks

Deep face representations effectively distinguish attacks from genuine attempts

Synthetic makeup data enhances training of the detection classifier

Abstract

Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.