  Face recognition algorithms have demonstrated very high recognition
performance, suggesting suitability for real world applications. Despite the
enhanced accuracies, robustness of these algorithms against attacks and bias
has been challenged. This paper summarizes different ways in which the
robustness of a face recognition algorithm is challenged, which can severely
affect its intended working. Different types of attacks such as physical
presentation attacks, disguise/makeup, digital adversarial attacks, and
morphing/tampering using GANs have been discussed. We also present a discussion
on the effect of bias on face recognition models and showcase that factors such
as age and gender variations affect the performance of modern algorithms. The
paper also presents the potential reasons for these challenges and some of the
future research directions for increasing the robustness of face recognition
models.
