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GazeEstimation_alpha

Model for 3D Gaze Estimation based on L2CS-Net

Demo

Gaze_Businesswoman.-.129427_hd.mp4

OverView

This is an appearance-based 3D gaze estimation method. It is a method to improve the accuracy of the conventional method, L2CS-Net.

To be presented at IEICE (as of 2023.02.02).

Benchmark Performance

Results for the Gaze360 dataset using Mean Angular Error(MAE) as the evaluation index are as follows.

L2CS-Net Ours
MAE(degrees) 10.41 10.30

Quick Start

A simple demonstration can be performed using a pre-trained model and a web camera.

  • Download the pre-trained models from here.
python demo.py --snapshot "./"

Argument: Give the path to the model's weight.

Usage

train

python train.py --image_dir "./" --label_dir "./"

Argument: Give the path to the images and labels in your environment.

test

python test.py --snapshot "./"

Argument: Give the path where the model you want to test is stored.

Requirements

  • Ubuntu : 20.04
  • Python : 3.7

Install another packages

pip install -r requirements.txt

Introduction

To improve the accuracy of the conventional method, L2CS-Net, we devised a method that uses face and both eye images as input. Our gaze estimation network is shown below. (These images are taken from Gaze360.)

introfig

The project contains follwing files/folders.

  • model.py : the model code.
  • train.py : the entry for training and validation.
  • test.py : the entry fot testing.
  • dataset.py : the data loader code.
  • utils.py : the utils code.

DataPreparing

  • Dowanload Gaze360 dataset.

  • Apply pre-processing to the dataset.

  • The path of the dataset should be ./datasets/Gaze360.

Citation

Gaze360:

@InProceedings{Kellnhofer_2019_ICCV,
	author = {Kellnhofer, Petr and Recasens, Adria and Stent, Simon and Matusik, Wojciech and Torralba, Antonio},
	title = {Gaze360: Physically Unconstrained Gaze Estimation in the Wild},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	month = {October},
	year = {2019}
}