# EETnet: a CNN for Gaze Detection and Tracking for Smart-Eyewear

arXiv: https://arxiv.org/abs/2511.04779 · HTML: https://mlsanity.com/paper/2511.04779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers](https://mlsanity.com/paper/2306.00001/) (2023)
- [High-frequency near-eye ground truth for event-based eye tracking](https://mlsanity.com/paper/2502.03057/) (2025)

## Abstract (arXiv)

Event-based cameras are becoming a popular solution for efficient, low-power eye tracking. Due to the sparse and asynchronous nature of event data, they require less processing power and offer latencies in the microsecond range. However, many existing solutions are limited to validation on powerful GPUs, with no deployment on real embedded devices. In this paper, we present EETnet, a convolutional neural network designed for eye tracking using purely event-based data, capable of running on microcontrollers with limited resources. Additionally, we outline a methodology to train, evaluate, and quantize the network using a public dataset. Finally, we propose two versions of the architecture: a classification model that detects the pupil on a grid superimposed on the original image, and a regression model that operates at the pixel level.