A Robust Pupil Localization via a Novel Parameter Optimization Strategy

Abstract

With the development of iris biometrics, the pupil recognition method is widely used in the fields of identity recognition and so on. Most traditional iris recognition algorithms use an adaptive threshold to the eye image binarization processing; although this can at the maximum preserve the original characteristics of the image itself, the retained features in this method included a lot of noise points, including Gaussian noise and so on, in a series of image preprocessing. The subsequent iris recognition and pupil positioning are still prone to the loss of positioning accuracy, which cannot separate the pupil completely from the background area of the image, increasing the difficulty of connecting the pupil to domain localization, thus affecting the pupil positioning accuracy. Therefore, in order to meet the requirement of high precision pupil recognition accuracy in low-quality eye images, this paper takes the iris dataset provided by the China Research Institute of Automation as an example to improve the traditional pupil location method. A pupil localization method based on parameter optimization is proposed, which includes the setting of a three-step threshold, called as TST.