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Neuromorphic Face Analysis: a Survey

Federico Becattini, Lorenzo Berlincioni, Luca Cultrera, Alberto Del Bimbo

TL;DR

This survey addresses the problem of applying neuromorphic (event) cameras to face analysis, highlighting how asynchronous, high-temporal-resolution data can improve latency and privacy compared with traditional RGB sensors. It provides a structured review of principles, data representations, and the current state-of-the-art across nine application areas, emphasizing both modular and end-to-end approaches and the role of synthetic versus real data. Key insights include the advantages of event streams for dynamic facial analyses (e.g., micro-expressions, gaze) as well as challenges from data scarcity, lack of benchmarks, and domain shifts when using RGB-trained detectors. The work outlines practical implications for privacy-preserving vision, AR/VR, driver monitoring, and edge computing, and points to future directions such as healthcare applications and robust emotion understanding with neuromorphic sensing.

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, 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.

Neuromorphic Face Analysis: a Survey

TL;DR

This survey addresses the problem of applying neuromorphic (event) cameras to face analysis, highlighting how asynchronous, high-temporal-resolution data can improve latency and privacy compared with traditional RGB sensors. It provides a structured review of principles, data representations, and the current state-of-the-art across nine application areas, emphasizing both modular and end-to-end approaches and the role of synthetic versus real data. Key insights include the advantages of event streams for dynamic facial analyses (e.g., micro-expressions, gaze) as well as challenges from data scarcity, lack of benchmarks, and domain shifts when using RGB-trained detectors. The work outlines practical implications for privacy-preserving vision, AR/VR, driver monitoring, and edge computing, and points to future directions such as healthcare applications and robust emotion understanding with neuromorphic sensing.

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, 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.
Paper Structure (15 sections, 2 figures, 2 tables)

This paper contains 15 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Event Camera and Face Analysis: Advantages and Challenges
  3. Data Representation
  4. Event Cameras as a Privacy Preservation Layer
  5. Present Research
  6. Face Detection
  7. Identity Recognition
  8. Face Pose Alignment
  9. LipReading & Voice Activity Detection
  10. Facial Expression & Emotion Recognition
  11. Gaze Analysis and AR/VR
  12. Driver Monitoring System
  13. Datasets
  14. Synthethic Approaches
  15. Conclusions and Future Developments

Figures (2)

  • Figure 1: Failure cases of computer vision models over event data. Top Row: Samples of face detection and landmark estimation on RGB frames. Bottom Row: Samples of face detection and landmark estimation on the corresponding Event frames.
  • Figure 2: Compression artifacts after simulation using Hu2021v2ecvprworkshopeventvision2021. Image courtesy of berlincioni2024neuromorphic