# Neuromorphic Event-based Facial Expression Recognition

arXiv: https://arxiv.org/abs/2304.06351 · HTML: https://mlsanity.com/paper/2304.06351/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 75 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 30 of 34)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [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)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://mlsanity.com/paper/2102.09320/) (2021)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [Temporal Binary Representation for Event-Based Action Recognition](https://mlsanity.com/paper/2010.08946/) (2020)
- [Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications](https://mlsanity.com/paper/1606.03237/) (2016)
- [DART: Distribution Aware Retinal Transform for Event-based Cameras](https://mlsanity.com/paper/1710.10800/) (2017)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (2022)
- [Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model](https://mlsanity.com/paper/1804.03675/) (2018)
- [Moving Object Detection for Event-based vision using Graph Spectral Clustering](https://mlsanity.com/paper/2109.14979/) (2021)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (2021)
- [Spatiotemporal Registration for Event-based Visual Odometry](https://mlsanity.com/paper/2103.05955/) (2021)
- [Event-based Face Detection and Tracking in the Blink of an Eye](https://mlsanity.com/paper/1803.10106/) (2018)
- [Lifting Monocular Events to 3D Human Poses](https://mlsanity.com/paper/2104.10609/) (2021)
- [Event-based attention and tracking on neuromorphic hardware](https://mlsanity.com/paper/1907.04060/) (2019)

## Built on by (most cited first, 18 of 18)

- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Neuromorphic Face Analysis: a Survey](https://mlsanity.com/paper/2402.11631/) (2024)
- [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)
- [Event Trojan: Asynchronous Event-based Backdoor Attacks](https://mlsanity.com/paper/2407.06838/) (2024)
- [Evaluating Image-Based Face and Eye Tracking with Event Cameras](https://mlsanity.com/paper/2408.10395/) (2024)
- [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)
- [CS3D: An Efficient Facial Expression Recognition via Event Vision](https://mlsanity.com/paper/2512.09592/) (2025)
- [Spike-TBR: a Noise Resilient Neuromorphic Event Representation](https://mlsanity.com/paper/2506.04817/) (2025)
- [Event-based Facial Keypoint Alignment via Cross-Modal Fusion Attention and Self-Supervised Multi-Event Representation Learning](https://mlsanity.com/paper/2509.24968/) (2025)
- [LiFR-Seg: Anytime High-Frame-Rate Segmentation via Event-Guided Propagation](https://mlsanity.com/paper/2603.21115/) (2026)
- [Locally Adaptive Decay Surfaces for High-Speed Face and Landmark Detection with Event Cameras](https://mlsanity.com/paper/2602.23101/) (2026)
- [Generative Anonymization in Event Streams](https://mlsanity.com/paper/2604.12803/) (2026)
- [Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach](https://mlsanity.com/paper/2604.26285/) (2026)
- [CDER-SME: A Cross-Device Event-RGB Micro-Expression Dataset under Multi-Level Stress Induction](https://mlsanity.com/paper/2606.20715/) (2026)

## Abstract (arXiv)

Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal.