SET: a pupil detection method using sinusoidal approximation. Javadi, A., Hakimi, Z., Barati, M., Walsh, V., & Tcheang, L. Frontiers in neuroengineering, 8:4, 2015.
SET: a pupil detection method using sinusoidal approximation [pdf]Paper  abstract   bibtex   
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 con-fines experiments to artificial environments where conditions must be tightly controlled. Ad-ditionally, the emergence of low-cost eye tracking devices calls for the development of analy-sis 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 envi-ronments with uncontrolled, dynamic and even extreme lighting conditions. We compared the performance of SET with that of two open-source alternatives by processing two collec-tions of eye images: images of natural outdoor scenes with extreme lighting variations (‘Natu-ral’); 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, in-doors. SET offers a low cost eye-tracking solution, delivering high performance even in chal-lenging 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 (

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