How to choose the size of facial areas of interest in interactive eye tracking

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer's test conditions, validation is essential with regard to the quality of gaze data and other f...

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Bibliographic Details
Published in:PloS one 2022-02, Vol.17 (2), p.e0263594-e0263594
Main Authors: Vehlen, Antonia, Standard, William, Domes, Gregor
Format: Article
Language:English
Subjects:
Accuracy
Adult
Attention
Biology and Life Sciences
Classification
Clinical psychology
Computer and Information Sciences
Errors
Eye
Eye Movements
Eye-Tracking Technology
Face
False alarms
Female
Fixation, Ocular
Gaze
Health aspects
Humans
Male
Measurement
Medical examination
Medicine and Health Sciences
Physical Sciences
Physiological aspects
Reproducibility of Results
Research and Analysis Methods
Simulation
Social Behavior
Social Interaction
Social Sciences
Standard deviation
Tessellation
Tracking
Young Adult
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Summary:Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer's test conditions, validation is essential with regard to the quality of gaze data and other factors potentially threatening the validity of this signal. In this study, we evaluated the impact of accuracy and areas of interest (AOIs) size on the classification of simulated gaze (fixation) data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying accuracy. As hypothesized, we found that accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed in falsely classified gaze inside AOIs (Type I errors; false alarms) and falsely classified gaze outside the predefined AOIs (Type II errors; misses). Our results indicate that smaller AOIs generally minimize false classifications as long as accuracy is good enough. For studies with lower accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of more probable Type I errors. Proper estimation of accuracy is therefore essential for making informed decisions regarding the size of AOIs in eye tracking research.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0263594