# Real-Time Face & Eye Tracking and Blink Detection using Event Cameras

arXiv: https://arxiv.org/abs/2010.08278 · HTML: https://mlsanity.com/paper/2010.08278/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 27 of 27)

- [SODFormer: Streaming Object Detection with Transformer Using Events and Frames](https://mlsanity.com/paper/2308.04047/) (2023)
- [3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network](https://mlsanity.com/paper/2308.11771/) (2023)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [Control and Evaluation of Event Cameras Output Sharpness via Bias](https://mlsanity.com/paper/2210.13929/) (2022)
- [Neuromorphic Face Analysis: a Survey](https://mlsanity.com/paper/2402.11631/) (2024)
- [FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker](https://mlsanity.com/paper/2406.03177/) (2024)
- [Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking](https://mlsanity.com/paper/2404.00548/) (2024)
- [Neuromorphic Sensing for Yawn Detection in Driver Drowsiness](https://mlsanity.com/paper/2305.02888/) (2023)
- [Evaluating Image-Based Face and Eye Tracking with Event Cameras](https://mlsanity.com/paper/2408.10395/) (2024)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [GazeSCRNN: Event-based Near-eye Gaze Tracking using a Spiking Neural Network](https://mlsanity.com/paper/2503.16012/) (2025)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing](https://mlsanity.com/paper/2409.18813/) (2024)
- [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://mlsanity.com/paper/2502.18490/) (2025)
- [Neuromorphic Seatbelt State Detection for In-Cabin Monitoring with Event Cameras](https://mlsanity.com/paper/2308.07802/) (2023)
- [Event-based Facial Keypoint Alignment via Cross-Modal Fusion Attention and Self-Supervised Multi-Event Representation Learning](https://mlsanity.com/paper/2509.24968/) (2025)
- [Benchmarking Microsaccade Recognition with Event Cameras: A Novel Dataset and Evaluation](https://mlsanity.com/paper/2510.24231/) (2025)
- [SynSacc: A Blender-to-V2E Pipeline for Synthetic Neuromorphic Eye-Movement Data and Sim-to-Real Spiking Model Training](https://mlsanity.com/paper/2602.08726/) (2026)
- [Event Fusion Photometric Stereo Network](https://mlsanity.com/paper/2303.00308/) (2023)
- [Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems](https://mlsanity.com/paper/2307.13600/) (2023)
- [Contactless Cardiac Pulse Monitoring Using Event Cameras](https://mlsanity.com/paper/2505.09529/) (2025)
- [Autobiasing Event Cameras for Flickering Mitigation](https://mlsanity.com/paper/2511.02180/) (2025)
- [Neuromorphic Eye Tracking for Low-Latency Pupil Detection](https://mlsanity.com/paper/2512.09969/) (2025)
- [Locally Adaptive Decay Surfaces for High-Speed Face and Landmark Detection with Event Cameras](https://mlsanity.com/paper/2602.23101/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## Abstract (arXiv)

Event cameras contain emerging, neuromorphic vision sensors that capture local light intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame based cameras and offers several significant advantages: low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advantages. This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring. A unique, fully convolutional recurrent neural network architecture is presented. To train this network, a synthetic event-based dataset is simulated with accurate bounding box annotations, called Neuromorphic HELEN. Additionally, a method to detect and analyse drivers eye blinks is proposed, exploiting the high temporal resolution of event cameras. Behaviour of blinking provides greater insights into a driver level of fatigue or drowsiness. We show that blinks have a unique temporal signature that can be better captured by event cameras.