SET: a pupil detection method using sinusoidal approximation

Abstract

Mobile eye-tracking in external environments remains challenging, despite recent advances in eye-tracking software and hardware engineering. Many current methods fail to deal with the vast range of outdoor lighting conditions and the speed at which these can change. This confines experiments to artificial environments where conditions must be tightly controlled. Additionally, the emergence of low-cost eye tracking devices calls for the development of analysis tools that enable non-technical researchers to process the output of their images. We have developed a fast and accurate method (known as “SET”) that is suitable even for natural environments with uncontrolled, dynamic and even extreme lighting conditions. We compared the performance of SET with that of two open-source alternatives by processing two collections of eye images: images of natural outdoor scenes with extreme lighting variations (“Natural”); and images of less challenging indoor scenes (“CASIA-Iris-Thousand”). We show that SET excelled in outdoor conditions and was faster, without significant loss of accuracy, indoors. SET offers a low cost eye-tracking solution, delivering high performance even in challenging outdoor environments. It is offered through an open-source MATLAB toolkit as well as a dynamic-link library (“DLL”), which can be imported into many programming languages including C# and Visual Basic in Windows OS (www.eyegoeyetracker.co.uk).

Oculomatic : High speed, reliable, and accurate open-source eye tracking for humans and non-human primates

“…In our experience, ellipse fitting algorithms (Swirski et al 2012; Javadi et al 2015) as well as Hough circle transformations tend to perform poorly on NHP eyes, especially when the pupil is dilated and partially-occluded by the eyelid. Contour finders and their associated image moments maintain functionality despite partial occlusion.…”

“…Since the software as well as hardware recommendations are transparent, it is easy for the user to change the setup to their particular need and application; we have focused on our primary need, namely tracking of head-fixed NHP ( Macaca mulatta ) eye movements. In designing the system, we have evaluated multiple existing eye tracking algorithms (Swirski et al 2012; Kassner et al 2014; Javadi et al 2015) for their ability to accurately track pupil location in NHPs as well as their computational burden. Our final solution uses binary image moments, an approach which is robust, easily modified to specific needs, and can be handled by affordable off-the-shelf hardware without relying on specialized implementations on embedded hardware systems.…”

Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

“…Wood et al [37] presented a model-based gaze estimation system for unmodified tablet computers. Three recent methods, SET [8], ExCuSe [5], and ElSe [6], explicitly address the aforementioned challenges associated with pupil detection in natural environments. Three recent methods, SET [8], ExCuSe [5], and ElSe [6], explicitly address the aforementioned challenges associated with pupil detection in natural environments.…”

“…The algorithms Starburst [18],Świrski [30], Pupil Labs [15], SET [8], ExCuSe [5], ElSe [6] will be presented and discussed in detail in the following subsections. We compared these algorithms on a large corpus of hand-labeled eye images (Sect.…”

iLid

“…However, there are a number of significant practical challenges with the use of these devices for continuous fatigue measurement. The first is that commercial wearable gaze trackers are well-known in the literature to fail under varying illumination conditions, especially outdoors [39, 66]. This makes them a poor choice for continuous gaze measurement in any environment other than an indoor setting.…”