Abstract
Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation tool that engages many users to interact through gamification and allows utilization of the crowd power to create reliable annotations \cite{artstein2005bias}. We describe the tool and the mechanisms employed, and report results on the annotation of a publicly available data set. Finally, we demonstrate an example utilization of the new high-quality annotation on a comparison of two state-of-the-art pupil center algorithms.
Highlights
Reliable annotated data sets are the cornerstones of the development of new algorithms in many disciplines, especially those related to computer vision and machine learning
In the domains of speaker recognition or machine translation (Greenberg et al, 2014; Przybocki et al, 2009), annual challenges are organized by independent bodies that create annotated data sets, to stimulate the research and push forward the state-of-the-art methods
This paper proposes a novel solution to the problem of pupil annotation by the implementation and evaluation of an online system that allows the crowdsourcing of the annotation data
Summary
Reliable annotated data sets are the cornerstones of the development of new algorithms in many disciplines, especially those related to computer vision and machine learning. Available annotated data sets facilitate comparison and benchmarking of new algorithms, methods and approaches. In the domains of speaker recognition or machine translation (Greenberg et al, 2014; Przybocki et al, 2009), annual challenges are organized by independent bodies that create annotated data sets, to stimulate the research and push forward the state-of-the-art methods. Pupil-based measurements have broad and numerous applications in behavioral sciences. (de Greef et al, 2009) uses and pupil estimations, along other data, to trigger responses in an adaptive system.
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