pupil-detection

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.

Official implementation of a free pupillometry platform called PupilEXT proposed in the article "PupilEXT: flexible open-source platform for high resolution pupillometry in vision research".

Robust video-based eye tracking using recursive estimation of pupil characteristics

Official implementation of the pupillometry system called PupilSense proposed in the article "PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild".

Real time pupil detection in noisy images

A keras port of swook/GazeML for pupil, iris and eye-lid detection

Detects pupil of the eye from the images/video and create a circle around it.

Mirror to https://es-git.cs.uni-tuebingen.de/santini/EyeRecToo

The Pupil Detection AI ML program is used to get the co-ordinates of eyes and detect the pupil region. It only works with human face images.

Official implementation of the affective mobile sensing system called FacePsy proposed in the article "FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings".

This repository contains a robust pupil tracking algorithm that incorporates RANSAC for better outlier rejection during extreme eye blink artifacts

React PD-Meter App (measures distance between pupils) with MediaPipe Face Mesh - internship project (2023)

Open source implementation of the PuRe pupil detector.

A deep convolutional neural network implementation for tracking eye movements in videos

Fast face detection, pupil detection and landmarks for Android without dependencies.

Tracking gaze in Virtual Reality headsets.

• This program calculates the attention span of students in online classrooms based on gaze tracking. • Input data from webcam - which after processing into text data using OpenCV & Pupil Detection - is fed into a simple neural network. • Pupil tracking was done using the Tensorflow Object Detection API. • 10 other features were also extracted with the help of simple computer vision techniques by using OpenCV & Dlib library. • Gaze tracking is successful even without requiring to initialize for the first time. • Makes 6 prediction per second. It is faster than many current models since it uses a text based simple neural network rather than a full blown CNN.

This repo contains implementation of CNN based regression network for Pupil Center Estimation in smartphones

The project involves developing a method for robust, real-time pupil detection using computer vision techniques in OpenCV and Python, focusing on increasing accuracy and detection speed by reducing false edges from eyelids, eyelashes, and hair, and is suitable for integration into embedded architectures.