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

Antonia Vehlen,Gregor Domes,William Standard,Guido Maiello

doi:10.1371/journal.pone.0263594

Abstract

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.

Highlights

Eye tracking, especially in its video-based form, has become a standard method for investigating visual attention in many research areas, including social neuroscience [1, 2], psychopharmacology [3,4,5] and virtual reality [6, 7]
Thereby, we focus on classification performance with respect to false-positives and false-negatives to derive recommendations for choosing areas of interest (AOIs) size depending on accuracy
Large AOIs with a radius of 2.0 ̊ resulted in correct classification above the chance level for most AOIs, whereas classification of fixation points on the nose AOI were more evenly distributed across all other AOIs, resulting in the highest percentages of false-negatives (12.9 to 48.4%)

Summary

Introduction

Especially in its video-based form, has become a standard method for investigating visual attention in many research areas, including social neuroscience [1, 2], psychopharmacology [3,4,5] and virtual reality [6, 7]. The issue of data quality and inadequate reporting standards seems to be increasingly relevant as the field advances to develop more naturalistic, interactive or face-to-face eye tracking applications [10,11,12]. Do these setups deviate further in design from the manufacturer’s test conditions, but when used in naturalistic interactions, other factors can affect the accuracy, such as movements accompanying facial expressions, speech or varying viewing distances.

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Conclusion