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Testing multiple polynomial models for eye-tracker calibration.

Carlos Lara-Alvarez¹, Fernando Gonzalez-Herrera²

¹Information Technology Laboratory, Center for Research and Advanced Studies, Parque Cientifico y Tecnologico Tecnotam Km, 5.5 carretera Cd. Victoria - Soto La Marina C.P., 87130, Cd. Victoria, Tamaulipas, Mexico. c.alberto.lara@gmail.com.

Behavior Research Methods

|May 30, 2020

Summary

This summary is machine-generated.

This study introduces a robust eye-tracker calibration method using MMransac and model selection criteria. This approach improves accuracy by effectively handling outliers and preventing overfitting in polynomial models.

Keywords:

Akaike information criterion Calibration Eye tracking

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Area of Science:

Human-Computer Interaction
Computer Vision
Data Science

Background:

Traditional eye-tracker calibration assumes error-free data, which can lead to significant bias if violated.
Polynomial functions are common for eye-tracker calibration, but high degrees risk overfitting and reduced smoothness.

Purpose of the Study:

To develop a robust eye-tracker calibration algorithm that addresses data outliers.
To enable effective model selection for polynomial eye-tracker calibration, even with erroneous data points.

Main Methods:

Modified Random Sample Consensus (MMransac) algorithm for robust fitting.
Integration of model selection criteria, including Akaike Information Criterion (AIC) and Kullback Information Criterion (KIC).
Testing the algorithmic approach with various model selection criteria.

Main Results:

The proposed MMransac algorithm achieves robust eye-tracker calibrations.
Combined robust fitting and model selection yield more accurate calibrations.
AIC and KIC prove effective in guiding model selection for improved accuracy.

Conclusions:

The combined robust fitting and model selection approach significantly enhances eye-tracker calibration accuracy.
This method provides a reliable solution for eye-tracker calibration in the presence of data outliers.
The algorithm facilitates better model selection for polynomial mappings in eye-tracking systems.