MorFacing: A Benchmark for Estimation Face Recognition Robustness to Face Morphing Attacks

Biometrics in the realm of face image modality, has seen significant advancements in recent decades, which was driven by the rise of deep learning techniques. With widespread deployment across various domains, including document security and user authentication, face recognition based systems are increasingly susceptible to presentation attacks. In this work we address the issue of estimating the robustness of face recognition systems to face morphing attacks. We revisit the definition of Mated Morph Presentation Match Rate metrics and develop the benchmarking utilities for these metrics on the novel dataset. Through extensive experiments conducted with our benchmark, we estimate the robustness of various public face recognition models to face morphing attacks. Furthermore, we evaluate the efficiency of different face morphing techniques in deceiving face recognition systems.