Face recognition is an essential technology in our daily lives as a contactless and convenient method of accurate identity verification. Processes such as secure login to electronic devices or identity verification at automatic border control gates are increasingly dependent on such technologies. The recent COVID‐19 pandemic has increased the focus on hygienic and contactless identity verification methods. The pandemic has led to the wide use of face masks, essential to keep the pandemic under control. The effect of mask‐wearing on face recognition in a collaborative environment is currently a sensitive yet understudied issue. Recent reports have tackled this by using face images with synthetic mask‐like face occlusions without exclusively assessing how representative they are of real face masks. These issues are addressed by presenting a specifically collected database containing three sessions, each with three different capture instructions, to simulate real use cases. The data are augmented to include previously used synthetic mask occlusions. Further studied is the effect of masked face probes on the behaviour of four face recognition systems—three academic and one commercial. This study evaluates both masked‐to‐non‐masked and masked‐to‐masked face comparisons. In addition, real masks in the database are compared with simulated masks to determine their comparative effects on face recognition performance.

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The recent COVID‐19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR solutions. However, such solutions can fail in certain processes, leading to the verification task being performed by a human expert. This work provides a joint evaluation and in‐depth analyses of the face verification performance of human experts in comparison to state‐of‐the‐art automatic FR solutions. This involves an extensive evaluation by human experts and 4 automatic recognition solutions. The study concludes with a set of take‐home messages on different aspects of the correlation between the verification behaviour of humans and machines.

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Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks.