Neuromorphic face analysis: A survey

Abstract

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, data representations, emerging challenges, and limitations that require further investigation. This paper aims to highlight the recent progress in this evolving field to provide researchers an all-encompassing analysis of the state of the art along with its problems and shortcomings. • Neuromorphic sensors are becoming more common in human-face related vision tasks. • As a new niche field it lacks overview papers helping researchers orient themselves. • We compare the relevant literature to classical methods and highlights pros and cons. • Several tasks and recently published open dataset are presented and compared. • Future research directions are provided after analyzing the relevant shortcomings.

Topics & Concepts