Effets de la distance, de la luminosité et de l’angle de calibration sur la précision et la justesse de lunettes d’eye-tracking : une étude exploratoire

Les systèmes d’eye-tracking permettent d’étudier les comportements visuels. Pour que les données de ces systèmes soient précises, la calibration est primordiale. Une bonne calibration se traduit par des valeurs de précision et de justesse faibles. Dans cette étude exploratoire, pour évaluer l’effet de la distance, de la luminosité et de l’angle de calibration sur la précision et la justesse de lunettes d’eye-tracking, des mesures ont été effectuées en champ visuel proche, intermédiaire et éloigné sur un écran de calibrage. Neuf combinaisons de calibration ont été testées selon un plan en carré latin. La distance, la luminosité et l’angle de calibration n’ont pas d’effet significatif sur la précision et la justesse des lunettes. A l’inverse, la précision et la justesse des lunettes sont meilleures en champ éloigné, qu’en champ proche et intermédiaire. Ces résultats montrent que les lunettes d’eye-tracking sont davantage adaptées à des études de terrain

Improving eye-tracking calibration accuracy using symbolic regression

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

ETCAL - A versatile and extendable library for eye tracker calibration

Recently eye tracking has become a popular technique that may be used for variety of applications starting from medical ones, through psychological, analyzinguser experience, ending with interactive games. Video based oculography (VOG) is the most popular technique because it is non-intrusive, can be use in users’ natural environment and are relatively cheap as it uses only classic cameras. There are already well established methods for eye detection on a camera capture. However, to be usable in gaze position estimation, this information must be associated with an area in an observer scene, which requires evaluating several parameters. These parameters are typically estimated during the process called calibration. The main purpose of the software described in this paper is to establish a common platform that is easy to use and may be used in different calibration scenarios. Apart from the normal regression based calibration the ETCAL library allows also to use more sophisticated methods like automatic parameters optimization or the automatic detection of gaze targets. The library is also easily extendable and may be accessed with a convenient Web/REST interface. Keywords: Eye tracking, Calibration, Librar