关键词: 瞳孔检测, Hough变换, 轮廓匹配, 红外视频, OpenCV

Abstract: In order to improve the precision of detection on the diameter of pupils in infrared eye videos, an exact pupil detection algorithm (Hough-Contour) combining Hough transformation and contour matching was proposed. Firstly, each image frame was grayed and filtered; secondly, the edge of the image was extracted and the initial circle was detected and taken as the pupil parameter by the revised Hough gradient method; finally, around the pupil, a circular contour whose position and radius varies in a limited range was used to match the pupil, realizing the calculation of pupil center's coordinate and diameter. In the phase of Hough transformation, the descending sort of candidate circle centers according to their accumulated values in Hough transformation was turned into searching for their maximum, in order to reduce the time consumption of this proceeding and the calculation of radius later. In the experiment, the threshold of the maximum in the array of accumulated values was searched and the image frames of closing eyes were excluded by this threshold. In the phase of contour matching, the experiment shows that if the range of the circular contour moving and stretching was assigned one tenth of the radius of the initial circle, and if the number of point pairs was assigned 40, the precision of detection on pupils would reach 99.8% from around 10% which was attained by OpenCV circle transformation. In the experiments on time performance, the proposed algorithm needed 60 ms to process one frame on the low-end computers, and the real-time detection on infrared eye videos can be achieved on the high-end computers.

Key words: pupil detection, Hough transformation, contour matching, infrared video, OpenCV