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Ocular torsion is a key problem in the understanding of many visual perceptual effects. However, since it is difficult to record, its integration with other sensorimotor signals is still poorly understood. Unfortunately, eyetracker systems are generally not dedicated to the monitoring of eye torsion. In addition, the classical methods used with video-based systems present some limits in the accuracy of torsion calculation. These limits are especially related to the detection of pupil center and the effects of pupil size changes. This article aims at (1) proposing a solution to analyze ocular torsion together with head roll using EyeLink II or similar equipment, (2) reviewing and adapting classical polar cross-correlation methods in order to improve the accuracy of torsion measurement, (3) providing a lower-cost method compared with the existing ones. Video sequences issued from the EyeLink II host computer monitor were recorded by means of a second computer equipped with a video acquisition card and converted into image sequences. Images were analyzed with algorithms of pupil center detection (median-based algorithm), torsion analysis (adapted polar cross-correlation method which takes into account pupil size variations) and marker tracking (head roll analysis). This method was tested on virtual eye images. Results are discussed with respect to other algorithms found in the literature.

Analyzing head roll and eye torsion by means of offline image processing