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A Fast and Precise Location Algorithm Research for the Pupil Center and the Corneal Reflection Spot Center |
Yu Luo1 , Liu Hongying1,2*, Xu Shuai1, Cai Jinzhi1, Pi Xitian1# |
1School of Biomedical Engineering,Chongqing University,Chongqing 400030,China; 2Chongqing Medical Electronic Engineering Technology Research Center,Chongqing 400030,China |
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Abstract The eye tracking system plays an important role in clinical medicine, in order to raise the recognition rate of the eye characteristic value in the system. A double filtration algorithm is innovatively proposed to locate the center of pupil and the center of corneal reflection in this paper. First, the acquired eyes image was processed by the pupil center location algorithm to reach binarization, and then the contour points with 100~300 was set as the threshold. Next, pupil contour was ellipse fitted using the least square ellipse fitting method, and the contour that was most close to the circle was used as the pupil contour to obtain pupil center. On this basis, the edge of the eye image in the certain range of the pupil center was detected, and then the external rectangular center of the contour was calculated. Taking the center of the pupil as the starting point for coordinate transformation, the corneal reflection spot center after converting coordinates was eventually calculated. This algorithm not only reduced the area of image processing to a certain extent, but also improvedthe processing speed and the recognition rate of reflection spot. The experimental results showed that the designed algorithm accurately located the pupil center and corneal reflex center in different conditions in real time. The maximum mean square deviation of pupil-corneal reflection vector was less than 0.74 pixels, and the average error was less than 0.53 pixels. The algorithm run at a speed of 12.8 ms/frame. The algorithm met the precision as well as showed very strong robustness, being of significance for the development of eye tracker.
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Received: 07 May 2016
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[1] 胡雅娉, 梁战华. 帕金森病患者伴快速动眼睡眠行为障碍的研究进展[J]. 中华行为医学与脑科学杂志, 2016(2): 183-187. [2] Bayhan HA, Bayhan SA, Can IZ. Comparison of central corneal thickness measurements with three new optical devices and a standard ultrasonic pachymeter[J]. International Journal of Ophthalmology, 2013, 7(2): 302-308. [3] Franco JG, De PJ, Gaviria AM, et al. Smooth pursuit eye movements and schizophrenia: Literature review[J]. Archivos De La Sociedad Espanola De Oftalmologia, 2014, 89(9): 361-367. [4] Guénolé F, Chevrier E, Stip E, et al. A microstructural study of sleep instability in drug-naive patients with schizophrenia and healthy controls: sleep spindles, rapid eye movements, and muscle atonia[J]. Schizophrenia Research, 2014, 155(1-3): 31-38. [5] Harrison J, Sumner P, Freeman T. Co-ordination of voluntary and automatic eye-movements:, Accuracy of saccades made during concomitant optokinetic, nystagmus[J]. Economic History Review, 2012, 66(3):693-714. [6] Lions C, Bui-Quoc E, Wiener-Vacher S, et al. Smooth pursuit eye movements in children with strabismus and in children with vergence deficits[J]. PLoS ONE, 2013, 8(12): e83972. [7] Shajari M, Bühren J, Kohnen T. Dynamic torsional misalignment of eyes during laser in-situ keratomileusis[J]. Albrecht Von Graes Archiv Für Ophthalmologie, 2016, 254(5): 1-6. [8] Morimoto C H, Flickner M. Real-time multiple face detection using active illumination[C]// Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble: IEEE, 2000:8-13. [9] Haro A, Flickner M, Essa I. Detecting and tracking eyes by using their physiological properties, dynamics, and appearance[J], 2000, 1: 163-168. [10] Daugman JG. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1993, 15(11): 1148-1161. [11] Wildes R P. Iris recognition: An emerging biometrie technology[J]. Proceedings of the IEEE, 1997, 85(9): 1348-1363. [12] Zhu D, Moore S T, Raphan T. Robust pupil center detection using a curvature algorithm[J]. Computer Methods & Programs in Biomedicine, 1999, 59(3): 145-157. [13] 陈健, 郑绍华, 潘林, 等. 结合椭圆拟合与参数传递的瞳孔检测方法[J]. 仪器仪表学报, 2014(8): 1890-1899. [14] 程成, 杜菁菁, 蓝飞翔. 眼动交互的实时线性算法构造和实现[J]. 电子学报, 2009, 37(b04): 12-15. [15] 周封, 杨超, 王晨光, 等. 基于随机Hough变换的复杂条件下圆检测与数目辨识[J]. 仪器仪表学报, 2013, 34(3): 622-628. [16] 宋晓宇, 袁帅, 郭寒冰, 等. 基于自适应阈值区间的广义Hough变换图形识别算法[J]. 仪器仪表学报, 2014, 35(5): 1109-1117. [17] 田野, 王怀军, 方志良. 基于形态学重构算法的瞳孔精确检测[J]. 光电子·激光, 2008, 19(3): 409-411. [18] 陈学军, 杨永明, 何为, 等. 一种应用于视频眼震分析的瞳孔中心定位方法[J]. 中国生物医学工程学报, 2012, 31(2): 184-189. [19] 张太宁, 孟春宁, 常胜江. 基于两次多项式拟合的人眼注视方向估计[J]. 光电子·激光, 2012, 23(7): 1389-1394. [20] 王静, 王海亮, 向茂生, 等. 基于非极大值抑制的圆目标亚像素中心定位[J]. 仪器仪表学报, 2012, 33(7): 1460-1468. [21] Dong Weihua, Liao hua, Xu Fang, et al. Using eye tracking to evaluate the usability of animated maps[J]. Science China Earth Science, 2014, 57(3): 512-522. |
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