Calibration Error Prediction: Ensuring High-Quality Mobile Eye-Tracking
Beibin Lia, James C. Sniderb, Quan Wangc, Sachin Mehtad, Claire E. Fostere, Erin C. Barneyf, Linda G. Shapirog, Pamela E. Ventolah, Frederick Shici
- aPaul G. Allen School of Computer Science and Engineering, University of Washington, United States and Microsoft Research, USA
- bSeattle Children's, United States
- cXi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China
- dElectrical and Computer Engineering, University of Washington, United States
- ePsychology, Binghamton University, United States
- fCogstate, United States
- gPaul G. Allen School of Computer Science and Engineering, University of Washington, United States
- hYale Child Study Center, Yale University, United States
- iPediatrics, University of Washington, United States and Center for Child Health, Behavior and Development, Seattle Children's Research Institute, United States
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Abstract
Gaze calibration is common in traditional infrared oculographic eye tracking. However, it is not well studied in visible-light mobile/remote eye tracking. We developed a lightweight real-time gaze error estimator and analyzed calibration errors from two perspectives: facial feature-based and Monte Carlo-based. Both methods correlated with gaze estimation errors, but the Monte Carlo method associated more strongly. Facial feature associations with gaze error were interpretable, relating movements of the face to the visibility of the eye. We highlight the degradation of gaze estimation quality in a sample of children with autism spectrum disorder (as compared to typical adults), and note that calibration methods may improve Euclidean error by 10%.
TL;DRA lightweight real-time gaze error estimator was developed and facial feature associations with gaze error were interpretable, relating movements of the face to the visibility of the eye. (Semantic Scholar)