Design and development of a performance evaluation framework for remote eye gaze estimation systems
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In this dissertation, a comprehensive evaluation framework for remote eye gaze estimation systems that are implemented in consumer electronics applications is developed. For this, firstly, a detailed literature review was made which helped to gain deep insights about the current state-of-the-art in eye gaze estimation algorithms and applications, by categorizing eye gaze research works into different consumer use cases. The wide range of existing gaze estimation algorithms were classified and their applications in interdisciplinary areas such as human computer interactions, cognitive studies and consumer electronics platforms like automotive, handheld devices, augmented and virtual reality were summarised. The review further identified the major challenges faced by contemporary remote gaze estimation systems, which include variable operating conditions such as user distance from tracker, viewing angle, head pose and platform movements that have significant impact on a gaze tracker’s performance. Other issues include deficit of common evaluation methodologies, standard metrics or any comprehensive tools or software which may be used for quantitatively evaluating gaze data quality and studying impact of the various challenging operating conditions on gaze estimation accuracy. Based on the outcomes of this review, the concept of a dedicated performance evaluation framework for remote eye gaze estimation systems was formulated. This framework was implemented in this thesis work through the following steps: a) defining new experimental protocols for collection of data from a remote eye tracker operating under several challenging operating conditions b) collection of gaze data from a number of participants using a commercial remote eye tracker under variable operating conditions c) development of a set of numerical metrics and visualization methods using the collected data to express gaze tracking accuracy in homogeneous units and quantitatively explore gaze data characteristics and quality d) implementing machine learning models using the collected gaze datasets to identify and predict error patterns produced in gaze data by different operating conditions e) development of a software and web-application that incorporates the developed metrics and visualization methods into user-friendly graphical interfaces f) creation of open source code and data repositories containing the performance evaluation tools and methods developed in this thesis, so that they can be used by researchers and engineers working with remote gaze estimation systems. The aim of this dissertation is to present a set of methods, data, tools and algorithms as analytical resources for the eye gaze research community to use for better understanding of eye tracking data quality, detection of anomalous gaze data and prediction of possible error levels under various operating conditions of an eye tracker. Overall, these methods are envisioned to improve the quality and reliability of eye tracking systems operating under practical and challenging scenarios in current and future consumer applications.