I’ve got https://a.co/d/0ezlgEOB an 3mm 850nm ir led about 1.5mm away from my eye on a 1k resistor, how safe is this for prolonged eye tracking on 5v usb

I am doing a computing project, and one option I am considering is basic eye tracking software. How viable is this for somebody who haa no experince in that specific field?

Hello everyone!

I am a broke medical student who needs some assistance finding a reliable eye tracker (~1 degree error). My budget is around $100

Have heard tobii 5 but was wondering if there are any other suggestions or maybe even custom builds. Saw one with PS3 cam but required heavy modification.

Anything helps! Thank you!

https://beam.eyeware.tech/beam-eye-tracker-2-6-native-linux-macos-support/

I'm a grad student trying to decide if my lab needs a new eye tracker for my research or if we could get by without. I want to analyze where participants are looking on their screens with about 2 degrees of accuracy, what's on that part of the screen at the time (if possible), and how long they spend looking away from the screen. All the options I've seen have been $4k+ and my lab doesn't have grant money to spend this year. Does anyone here know if there is affordable software for analyzing eye gaze from webcam footage?

So I’ve tried doing research but I couldn’t find anything about this. (I suck at researching lol)

I want to stream games to my friends on discord with a bubble of where I’m looking + audio but there seems to always be a downside, like either I also have to see the bubble or I have to turn on a virtual camera and they have to tune into my stream while looking at my camera.

Has anyone figured out the best solution?

Also I was going to get a tobii 5 but if someone has another recommendation I would love to hear it! (I don’t care to spend a bit)

What this report contains: a survey of open-source solutions that can be used to build a web application with webcam-based on-screen gaze prediction. Every solution gets a uniform fact sheet covering: runtime (does it run in the web browser, on a server, or on the desktop), reported accuracy, what benchmarks were run, what data the model was trained on, whether it uses only the camera image as input, official research articles describing the solution, a few lines on how it is built and its unique properties, plus license, maintenance status, and links. It ends with a comparison matrix, recommendations (up to 3 best solutions per runtime/use case), reference architectures, licensing guidance, and risks. A glossary of the cited research articles follows the fact sheets (§4).

How the projects were validated: every entry was checked against its primary source on 2026-08-06 — the GitHub API (stars, SPDX license, last push, archived status), the npm/PyPI registries, official documentation, and the original papers. Claims that could not be verified are explicitly flagged. Accuracy figures are as reported by the authors on their own benchmarks and are not directly comparable across datasets (centimeters on mobile phones ≠ degrees on desktop ≠ pixels on a screen).

1. Executive Summary

No single maintained, permissively-licensed, drop-in library gives research-grade webcam gaze in the browser today. The field splits into three practical strategies:

Key verdicts:

• WebGazer.js — the most famous library — is GPL-3.0 (not MIT, a common misconception) and its official maintenance ended February 2026. Its accuracy also degrades over time without head-pose handling (error grows from ~5 to ~10 cm during a 20-minute session, ETRA 2018). Prototypes only.
• WebEyeTrack (2025, Vanderbilt et al., MIT) is the most interesting modern turnkey option: browser-native CNN (BlazeGaze, 670 KB), MAML meta-learning + on-device few-shot calibration (<9 samples), 2.32 cm on GazeCapture, 2.4 ms inference on iPhone 14. Young project (npm 0.0.x) — validate before committing.
• MediaPipe Face Landmarker (Apache-2.0, Google-maintained) is the safest foundation: 478 landmarks including iris + head-pose transformation matrix, official Web (WASM) and Python APIs. You add the gaze mapping yourself (ridge regression / polynomial / small TF.js net) — exactly what RealEye's webcam-eyetracker-light-open does (17-point calibration, ≈120 CSS px).
• Server-side: L2CS-Net (MIT) is the accuracy reference for appearance-based gaze (3.92° MPIIGaze); yakhyo/gaze-estimation provides maintained ONNX exports (runnable in-browser via onnxruntime-web/WebGPU); EyeTrax (MIT) adds calibration routines and Kalman/EMA/KDE smoothing.
• EyeGestures (GPL-3.0, Rust-based engine, 20-point calibration) is the most complete calibrated browser gaze library besides RealEye's — great for prototypes/research, copyleft blocks commercial SaaS embedding.
• Licensing traps: research-only datasets (GazeCapture, MPIIGaze, EyeDiap) are used to train most pretrained models — verify weight provenance before commercial use; camgaze.js has no license file (legally unusable as-is).

Open-Source Webcam Eye Tracking for Web Applications · RealEye-io/community

Good Morning

My name is Rory and I am a Cricketer and field Hockey player from Australia

I would love to chat to you about quiet eye and ball tracking drills to help with my chosen sports.

Any help would be much appreciated

Kind regards

Does anyone have one that is working well? I'd be more than willing to buy it from you, it's a necessity for me to do my job, and I'm currently using a Mini that isn't working great

BLINK uses your webcam to measure how long you can keep your eyes open.

The moment you blink, the test ends. It then compares your “human uptime” with an average person and estimates how much of your waking lifetime would disappear if you always blinked at that rate.

Using the project’s deliberately simplified model, an average human blinks about 15 times per minute and spends almost two years (crazy!) of an 80-year lifetime with their eyes closed while awake.

There’s also a global leaderboard, because apparently even disappearing can become competitive.

Camera processing happens locally in the browser. Nothing is uploaded.

https://absurd.website/blink/

Where do we go when we blink? 😄

i know it is a bit offtopic, sorry for that

Hey all!
I am setting up a study integrating pupilometry. I’m used to using the Eyelink software/ equipment that during calibration and validation informs you whether these measurements were good or poor.
I now switched to using Tobii pro Fusion. I’ve set up my experiment in Psychopy using Titta to integrate the eye tracker. Unfortunately, this way I don’t get a direct metric of whether my calibration/ validation was good or not. What I get is what’s shown in the picture. Any tips on reference points for these numbers? For example degrees above or below x should be acceptable, etc
Thank you all ☺️

Built this prototype to explore affordable, modular assistive mobility. Feedback from engineers, makers, and accessibility enthusiasts is welcome.

You can check out the full project entry and details here: https://contest.techbriefs.com/2026/entries/robotics-and-automation/14515-0709-035951-phas-modular-eye-gaze-assistive-mobility-platform-for-affordable-wheelchair-independence

I found some free platforms for image analysis with predictive eye tracking, but I haven’t been able to find any for videos (Attention Insight only allows me to upload 10 seconds of video)
Does anyone know of any? Thanks!

Hello, as part of my curriculum I am doing an eyetracker study. It measures browser events vs. gaze
I have programmed the code for this myself using a mixture of JS and python . The JS sends events via http to the bridge, which then sends them to an eyetracker using pylink.

I align the eyetracker clock and the browser clock with a linear fit.

I think it works pretty okay, but I have one worry.

How can I measure the time it takes for the message to go from client to server?

Because this might give me an offset of some milliseconds, which I might need to account for.

I hope my question is somewhat understandable.

Thank you a lot!

Best Regards!

Hello everyone,

I am a software developer from Brazil currently working on an open-source assistive technology project aimed at helping people with motor disabilities interact with computers more independently.

One of the main features I plan to implement is eye tracking using a standard webcam, allowing users to control certain aspects of the interface without relying on traditional input devices such as a mouse or keyboard.

As I continue developing this application, I would like to learn from people who have experience with eye tracking systems, accessibility software, or assistive technologies in general.

What are the most important factors I should consider during development? For example:

• Accuracy and calibration methods
• User fatigue during prolonged use
• Interface design and accessibility considerations
• Cursor control and click mechanisms
• Common usability issues
• Hardware limitations when using standard webcams
• Any lessons learned from existing eye-tracking solutions

I would greatly appreciate any advice, resources, research papers, personal experiences, or recommendations that could help make this project more useful and accessible for its intended users.

Thank you very much for your time and support!

Hi friends! This company is trying to make eye-tracking/assistive tech devices affordable: www.babatech.io

Full disclaimer: I know the people who started it personally and they're very open to feedback. Tried the demo and it's pretty cool what they're trying to do

I’m trying to understand different eye-tracking approaches for wearable glasses. In particular, I want to know the practical differences between **optical eye tracking** (using cameras/IR and gaze estimation) and **EOG (electrooculography)** (measuring eye-movement voltages). From an implementation perspective (hardware complexity, cost, power consumption, calibration, and achievable accuracy), which one is easier to build into glasses and why?

Hi guys,

​

You might find this interesting,

​

https://github.com/RealEye-io/webcam-eyetracker-light-open

​

​

RealEye Webcam EyeTracker Light Open is a webcam eye-tracker running in your web browser that provides on-screen gaze coordinates. It works with any regular camera — laptop built-in webcam, USB webcam, or mobile device camera. Everything runs locally on your device. No external servers, no dedicated eye-tracking hardware required. Built in TypeScript with custom ridge regression.

​

Demo app for desktop devices: https://github.com/RealEye-io/webcam-eyetracker-light-open

​

​

​

https://youtu.be/feVK53ONNok

​

What do you think?