Novel eye feature extraction and tracking for non-visual eye-movement applications

The Neuro-Linguistic Programming (NLP) Eye-Accessing Cues (EAC) model suggests that there is a correlation between eye-movements and the internal processing mode that people employ when accessing their subjective experience. Upon careful examination, the experimental methodologies of past research studies were based on assumptions informed by an incomplete or erroneous understanding of the EAC model that could have significantly influenced the experimental results. The reliability of the results can be further impacted by the absence of modern eye-tracking equipment to support the inherently complex task of reliably recording, selecting and rating eye-movements. While a plethora of eye-tracker designs is available to date, none of them has been designed to track non-visual eye-movements (eye-movements that are a result of neuro-physiological events and are not associated with vision), which tend to range outside the normal visual field and thus perform poorly in such cases. Therefore, this thesis introduces a set of novel algorithms for the extraction of relevant eye features (pupil position, iris radius and eye corners) that are combined to calculate the 2D gaze direction and to classify each eye-movement to one of eight classes from the EAC model. The applicability of the eyetracker is demonstrated through a pilot study that serves as a real-world application case study. The performance of the eye-tracker is found to be practical for the intended purpose as it is lightweight, low-cost and can robustly perform the tasks of 2D gaze direction estimation and classification.

Name dc:type.qualificationname

d_ph

Level dc:type.qualificationlevel

d_ph

Grantor dc:publisher.institution

University of Birmingham

Year dc:date.issued

2010

Author dc:creator

• Diamantopoulos, Georgios

• TK Electrical engineering. Electronics Nuclear engineering