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Abstract
In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.
Highlights
The first experiments using eye trackers began in early twentieth century [1]
The fast robust ellipse detection algorithm (FREDA) I is superior to the other algorithms, up to a precision of 2 pixel error, which was closely followed by the FREDA II algorithm
Their performance on a large set of images obtained under a wide variety of conditions, the FREDA I showed greater precision in the detection of the pupil center, surpassing the ELSe algorithm, which has been used as a reference among the published algorithms to date
Summary
The first experiments using eye trackers began in early twentieth century [1]. At that time, gaining an understanding of eye movements was one of the main objectives of those evaluations [2]. The technology has evolved, considerably widening the range of applications for which eye trackers can be employed. As the computational capacity of the existing equipment increases and as the price of the available technology decreases, more powerful and computationally expensive algorithms have been introduced for eye tracker devices. The range of applications using eye trackers has become wider, including human–computer interaction and eye movement analysis. Over the last few years, considerable efforts have been made to broaden the use of this technology to new application environments. Making this technology more robust and cheaper is key in order to apply this knowledge to conditions
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