FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 95.73% mean spatial overlap

• Primary Outcome: Spatial overlap between automated and expert-defined AOIs

• Nnt Or Sensitivity: >98.8% fixation capture rate for key AOIs

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility with open-source availability and user-friendly interface requiring no programming skills Valuable for Australian developmental psychology and autism research centres, particularly relevant for ASD diagnostic research supported by NDIS funding streams Researchers conducting eye-tracking studies in face perception across developmental populations

Abstract

Face perception is fundamental to human social interaction, relying on the dynamic allocation of visual attention to facial features across natural variations in poses, expressions, lighting and context-critical factors for real-world face perception. While eye-tracking studies have advanced our understanding of this process, analyzing gaze data in naturalistic settings presents challenges due to the labor-intensive and often subjective process of manually defining areas of interest (AOI) in dynamic stimuli. We introduce FaceTrack-AOI, an open-source, artificial intelligence (AI)-driven tool that automates dynamic AOI construction using robust 68-point facial landmark detection. Featuring a user-friendly graphical interface, the tool allows researchers to define and export eye movement metrics without programming expertise. It supports both rectangular and polygon-based AOIs, with customizable buffers, blank zones, and hierarchical configurations, accommodating diverse theoretical and methodological needs. Validation on multi-ethnic naturalistic video frames, images from a standardized face dataset (Chinese Face and Body Dataset [CFBD]), and benchmarks against a published study revealed strong agreement with expert-defined manual AOIs, achieving a mean spatial overlap of 95.73%. A proof-of-concept validation confirmed high fixation capture: over 98.8% of dwell time was correctly directed toward key AOIs. In two experiments with infants (6-18 months) and adults viewing 28,800 dynamic video frames, and children (2-8 years), both neurotypical and with autism spectrum disorder (ASD), viewing 80 static faces, FaceTrack-AOI demonstrated enhanced sensitivity in detecting developmental and group differences in naturalistic face recognition. By integrating AI-powered tracking with an intuitive interface, FaceTrack-AOI streamlines gaze analysis for dynamic facial stimuli, offering researchers an efficient and scalable solution to advance face perception research in ecologically valid contexts.

References

1. 1.Zhou, X., Chen, G., Ning, J., Cai, Y., Zhao, S., Zhao, S., Lu, Q., Chen, L., & Moulson, M. C. (2026). FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies. Behavior Research Methods. https://doi.org/10.1177/09567976211030630