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摘要: 基于圖像處理的方法，采用了由粗及精的瞳孔定位思想，提出了一種高精度的瞳孔定位算法。該算法首先利用瞳孔區域的直方圖，采用改進的最大類間方差法自適應地分割瞳孔區域，實現粗略定位，然后利用瞳孔灰度的梯度特性來精確定位瞳孔邊緣點，最后在像素級瞳孔邊緣點的基礎上，采用亞像素定位方法，更精確地求得亞像素級瞳孔邊緣點，并通過橢圓擬合的方法來精確確定瞳孔的中心位置。另外，針對瞳孔被遮擋的情況，本文提出了一種等距離補償瞳孔的方法。多次實驗結果證明了該算法對遮擋瞳孔的定位有較強的魯棒性，可以準確地定位瞳孔的位置。Abstract: The gaze tracking technology is widely used in many fields, and it has a broad application prospect in the field of human-computer interaction. The technology is based on the eye characteristic parameters and the gaze parameters, and it estimates the direction of sight and placement of sight based on the eye model. Therefore, accurately locating the pupil position is important in the gaze tracking technology, and it directly affects the accuracy of the gaze tracking result. Presently, there are numerous algorithms used in eye detection; however, most of them are characterized by some problems, such as the low accuracy of locating the pupil position, high detection error, and slow operation speed; thus, they cannot meet the accuracy requirements of locating the pupil position. To solve these problems, in this study, a concept of pupil localization method from rough to precise was adopted, and a high-accuracy pupil localization method based on image processing was proposed. In this method, first, the improved maximal between-cluster variance algorithm used the histogram of the pupil region to adaptively segment region to roughly locate the pupil region. Then the pupil edge points can be accurately located by the gradient of the pupil grayscale. Finally, a sub-pixel localization method was adopted on the basis of the pixel level edge points of the pupil to locate the sub-pixel level edge points of pupil more accurately, and the center position of the pupil was accurately determined by the method of ellipse fitting. In addition, an equidistance pupil compensation method was proposed in this paper for the situation of pupil occlusion. Several experimental results show that the algorithm is robust to locate the position of pupil occlusion and that it can achieve accurate pupil localization.
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圖 19 圖像處理結果. (a) 人眼圖像; (b) 二值化; (c) 腐蝕膨脹; (d) 瞳孔粗定位; (e) 瞳孔精確分割; (f) 瞳孔擬合
Figure 19. Results of image processing: (a) eye image; (b) binarization; (c) erosion and dilation; (d) pupil coarse localization; (e) accurate pupil segmentation; (f) pupil fitting
表 1 算法檢測誤差比較
Table 1. Comparison of measurement errors between algorithms
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表 2 瞳孔定位算法速度比較
Table 2. Comparison pupil localization speeds between algorithms
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表 3 瞳孔定位算法精度比較
Table 3. Comparison of pupil localization accuracies between algorithms
算法 定位精度/% 一般質量圖片 低質量圖片 文獻[9]方法 92.0 67.5 本文方法 96.0 78.5
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