# Bayesian Identification of Fixations, Saccades, and Smooth Pursuits

arXiv: https://arxiv.org/abs/1511.07732 · HTML: https://mlsanity.com/paper/1511.07732/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types](https://mlsanity.com/paper/2102.02115/) (2021)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture](https://mlsanity.com/paper/2105.00916/) (2021)
- ['Labelling the Gaps': A Weakly Supervised Automatic Eye Gaze Estimation](https://mlsanity.com/paper/2208.01840/) (2022)

## Abstract (arXiv)

Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms. Thus, an automatic and efficient algorithm to identify these eye movements is paramount for eye-tracking research involving dynamic stimuli. In this paper, we propose the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel algorithm for ternary classification of eye movements that is able to reliably separate fixations, saccades, and smooth pursuits in an online fashion, even for low-resolution eye trackers. The proposed algorithm is evaluated on four datasets with distinct mixtures of eye movements, including fixations, saccades, as well as straight and circular smooth pursuits; data was collected with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets. The algorithm exhibits high and consistent performance across all datasets and movements relative to a manual annotation by a domain expert (recall: μ = 91.42%, σ = 9.52%; precision: μ = 95.60%, σ = 5.29%; specificity μ = 95.41%, σ = 7.02%) and displays a significant improvement when compared to I-VDT, an state-of-the-art algorithm (recall: μ = 87.67%, σ = 14.73%; precision: μ = 89.57%, σ = 8.05%; specificity μ = 92.10%, σ = 11.21%). For algorithm implementation and annotated datasets, please contact the first author.