Makeup Presentation Attack Potential Revisited: Skills Pay the Bills
Pawel Drozdowski, S. Grobarek, J. Schurse, Christian Rathgeb, Fabian Stockhardt, Christoph Busch · 2021
Facial appearance can be substantially altered through the application of facial cosmetics. In addition to the widespread, socially acceptable, and in some cases even expected use for the purpose of beautification, facial cosmetics can be abused to launch so-called makeup presentation attacks. Thus far, the potential of such attack instruments has generally been claimed to be relatively low based on experimental evaluations on available datasets. This paper presents a new dataset of such attacks with the purpose of impersonation and identity concealment. The images have been collected from online sources, concentrating on seemingly highly skilled makeup artists. A vulnerability assessment of face recognition with respect to probe images contained in the collected dataset is conducted on state-of-the-art open source and commercial off-the-shelf facial recognition systems with a standardised methodology and metrics. The obtained results are especially striking for the impersonation attacks: the obtained attack success chance of almost 70% at a fixed decision threshold corresponding to 0.1% false match rate is significantly higher than results previously reported in the scientific literature.