# GazeSCRNN: Event-based Near-eye Gaze Tracking using a Spiking Neural Network

arXiv: https://arxiv.org/abs/2503.16012 · HTML: https://mlsanity.com/paper/2503.16012/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks](https://mlsanity.com/paper/2005.11633/) (2020)
- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network](https://mlsanity.com/paper/2308.11771/) (2023)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding](https://mlsanity.com/paper/2404.01174/) (2024)
- [Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs](https://mlsanity.com/paper/2408.12978/) (2024)

## Built on by (most cited first, 3 of 3)

- [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)
- [Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware](https://mlsanity.com/paper/2511.20175/) (2025)
- [Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach](https://mlsanity.com/paper/2604.26285/) (2026)

## Abstract (arXiv)

This work introduces GazeSCRNN, a novel spiking convolutional recurrent neural network designed for event-based near-eye gaze tracking. Leveraging the high temporal resolution, energy efficiency, and compatibility of Dynamic Vision Sensor (DVS) cameras with event-based systems, GazeSCRNN uses a spiking neural network (SNN) to address the limitations of traditional gaze-tracking systems in capturing dynamic movements. The proposed model processes event streams from DVS cameras using Adaptive Leaky-Integrate-and-Fire (ALIF) neurons and a hybrid architecture optimized for spatio-temporal data. Extensive evaluations on the EV-Eye dataset demonstrate the model's accuracy in predicting gaze vectors. In addition, we conducted ablation studies to reveal the importance of the ALIF neurons, dynamic event framing, and training techniques, such as Forward-Propagation-Through-Time, in enhancing overall system performance. The most accurate model achieved a Mean Angle Error (MAE) of 6.034deg and a Mean Pupil Error (MPE) of 2.094 mm. Consequently, this work is pioneering in demonstrating the feasibility of using SNNs for event-based gaze tracking, while shedding light on critical challenges and opportunities for further improvement.