Comparison of mapping algorithms for implicit calibration using probable fixation targets

Abstract

With growing access to cheap low end eye trackers using simple web cameras, there is also a growing demand on easy and fast usage of this devices by untrained and unsupervised end users. For such users the necessity to calibrate the eye tracker prior to its first usage is often perceived as obtrusive and inconvenient. In the same time perfect accuracy is not necessary for many commercial applications. Therefore, the idea of implicit calibration attracts more and more attention. Algorithms for implicit calibration are able to calibrate the device without any active collaboration with users. Especially, a real time implicit calibration, that is able to calibrate a device on-the-fly, while a person uses an eye tracker, seems to be a reasonable solution to the aforementioned problems. The paper presents examples of implicit calibration algorithms (including their real time versions) based on the idea of probable fixation targets (PFT). The algorithms were tested during a free viewing experiment and compared to the state of the art PFT based algorithm and explicit calibration results.