Master Face Generation Using Latent Variable Evolution in Face Recognition System

Face authentication is now widely used, especiallyon mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentationattacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that matchmultiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we perform an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. We run an LVE algorithm for various scenarios and with more than one database and/or face recognition system tostudy the properties of the master faces and to understand inwhich conditions strong master faces could be generated. Moreover, through analysis, we hypothesize that master faces come from some dense areas in the embedding spaces of theface recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserve the false-matching ability of their original digital forms, thus demonstrating that the existence of master facesposes an actual threat.