  Neuromorphic sensors, also known as event cameras, are a class of imaging
devices mimicking the function of biological visual systems. Unlike traditional
frame-based cameras, which capture fixed images at discrete intervals,
neuromorphic sensors continuously generate events that represent changes in
light intensity or motion in the visual field with high temporal resolution and
low latency. These properties have proven to be interesting in modeling human
faces, both from an effectiveness and a privacy-preserving point of view.
Neuromorphic face analysis however is still a raw and unstructured field of
research, with several attempts at addressing different tasks with no clear
standard or benchmark. This survey paper presents a comprehensive overview of
capabilities, challenges and emerging applications in the domain of
neuromorphic face analysis, to outline promising directions and open issues.
After discussing the fundamental working principles of neuromorphic vision and
presenting an in-depth overview of the related research, we explore the current
state of available data, standard data representations, emerging challenges,
and limitations that require further investigation. This paper aims to
highlight the recent process in this evolving field to provide to both
experienced and newly come researchers an all-encompassing analysis of the
state of the art along with its problems and shortcomings.
