Abstract
Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.
Highlights
With the continuous development of computer vision and artificial intelligence technology, human-eye-tracking techniques are increasingly used in clinical medicine [1], psychology [2,3], recognition systems [4,5], human–computer interaction [6,7], and other fields
The pupil is an important feature of the human eye, and pupil detection is often required in the sight-tracking process to perform sight estimation with the relative motion changes of the pupil
This paper proposes several improvements, mainly including the introduction of global distribution information in the form of a Gaussian model, combined with local intensity distribution information, and studies a fuzzy c-means clustering algorithm based on distribution information to more accurately segment the pupil pixels and for accurate pupil localization and sight estimation
Summary
With the continuous development of computer vision and artificial intelligence technology, human-eye-tracking techniques are increasingly used in clinical medicine [1], psychology [2,3], recognition systems [4,5], human–computer interaction [6,7], and other fields. There are many methods for pupil feature extraction, such as the threshold method [4,8,9], region method [10], random field method [11], neural network method [5], and clustering method [12]. These methods segment different types of images to different degrees. The poor quality of the pupil characteristics of the eye image, including the invasion of low-contrast objects, the invasion of high-intensity objects, and the low contrast between the pupil and the iris, leads to the missing edge information and inaccurate segmentation of the segmentation target
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