  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.
