A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
It addresses the lack of standardized evaluation methods for gaze tracking systems, which is an incremental improvement for researchers and developers in human-computer interaction.
The paper reviews eye-gaze estimation systems across consumer platforms, identifying platform-specific factors affecting accuracy, and proposes a methodological framework for standardized performance evaluation to ensure consistency in specifications and comparisons.
In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.