Improving eye-tracking calibration accuracy using symbolic regression.

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 app

SUBMITTER: Hassoumi A 

PROVIDER: S-EPMC6420251 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature