Abstract
The vulnerability of face recognition systems towards evolving presentation attacks has drawn significant interest in the last decade. In this paper, we present an empirical study on both vulnerability analysis and presentation attack detection for commercial face recognition systems (FRS) using custom 3D silicone face masks corresponding to real subjects. To this end, a new database is collected consisting of 8 custom 3D silicone masks together with bona fide presentations of the corresponding subjects using three different devices (smartphones). The vulnerability of FRS for 3D face silicone face masks is effectively evaluated using two well-known commercial-off-the-shelf (COTS) FRS (Verilook from Neurotechnology, and the Cognitec Face-VACS). Further, extensive experiments are carried out to evaluate the effectiveness of five state-of-the-art presentation attack detection (PAD) techniques for detecting such masks. Key insights on silicone mask PAD are provided along with a discussion on the accuracy achieved in our experiments.
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