ArticlePloS one2019
Improving eye-tracking calibration accuracy using symbolic regression.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory.Behavior research methods · 2026Article
- Immersive Rehabilitation Therapy (MoveR) Improves Postural and Visuo-Attentional Skills in Children with ADHD: A Clinical Study.Life (Basel, Switzerland) · 2026Article
- Gaze Dispersion During a Sustained-Fixation Task as a Proxy of Visual Attention in Children with ADHD.Vision (Basel, Switzerland) · 2025Article
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- Implementing mobile eye tracking in psychological research: A practical guide.Behavior research methods · 2024Review
- A new robust multivariate mode estimator for eye-tracking calibration.Behavior research methods · 2023Article
- Testing different function fitting methods for mobile eye-tracker calibration.Journal of eye movement research · 2023Article
- Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population.Journal of clinical medicine · 2022Article
- An eye tracking based virtual reality system for use inside magnetic resonance imaging systems.Scientific reports · 2021Article
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Authors and funding
3 authors.
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Abstract
Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations. These approaches allowed collecting more data compared to the standard 9-point calibration. However, the computation of the mapping function which provides planar gaze positions from pupil features given as input is mostly based on polynomial regressions, and little work has investigated alternative approaches. This paper fills this gap by providing a new calibration computation method based on symbolic regression. Instead of making prior assumptions on the polynomial transfer function between input and output records, symbolic regression seeks an optimal model among different types of functions and their combinations. This approach offers an interesting perspective in terms of flexibility and accuracy. Therefore, we designed two experiments in which we collected ground truth data to compare vestibulo-ocular and smooth pursuit calibrations based on symbolic regression, both using a marker or a finger as a target, resulting in four different calibrations. As a result, we improved calibration accuracy by more than 30%, with reasonable extra computation time.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.