# Neuromorphic Face Analysis: a Survey

arXiv: https://arxiv.org/abs/2402.11631 · HTML: https://mlsanity.com/paper/2402.11631/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [Temporal Binary Representation for Event-Based Action Recognition](https://mlsanity.com/paper/2010.08946/) (2020)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition](https://mlsanity.com/paper/2306.11546/) (2023)
- [Event-based Face Detection and Tracking in the Blink of an Eye](https://mlsanity.com/paper/1803.10106/) (2018)
- [Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging](https://mlsanity.com/paper/2306.03369/) (2023)
- [RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network](https://mlsanity.com/paper/2303.10770/) (2023)
- [Neuromorphic Sensing for Yawn Detection in Driver Drowsiness](https://mlsanity.com/paper/2305.02888/) (2023)
- [LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading](https://mlsanity.com/paper/2304.01080/) (2023)

## Built on by (most cited first, 9 of 9)

- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Neuromorphic Facial Analysis with Cross-Modal Supervision](https://mlsanity.com/paper/2409.10213/) (2024)
- [Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis](https://mlsanity.com/paper/2508.11988/) (2025)
- [Evaluating Image-Based Face and Eye Tracking with Event Cameras](https://mlsanity.com/paper/2408.10395/) (2024)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [Spatio-temporal Transformers for Action Unit Classification with Event Cameras](https://mlsanity.com/paper/2410.21958/) (2024)
- [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://mlsanity.com/paper/2502.18490/) (2025)
- [Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach](https://mlsanity.com/paper/2604.26285/) (2026)

## Abstract (arXiv)

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.