Robust pupil center detection using a curvature algorithm.
Robust pupil center detection using a curvature algorithm.
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DOI：
10.1016/s0169-2607(98)00105-9
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发表时间：
1999-06
影响因子：
6.1
通讯作者：
Danjie Zhu;Steven T. Moore;Steven T. Moore;Theodore Raphan;Theodore Raphan
中科院分区：
文献类型：
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作者：
Danjie Zhu;Steven T. Moore;Steven T. Moore;Theodore Raphan;Theodore Raphan
在基于视频的系统中，瞳孔中心的确定是计算眼睛方向的基础。由于眼睑、睫毛、角膜反射或阴影在许多情况下会遮挡瞳孔，现有的技术容易出错，也不可靠。我们开发了一种新的算法，利用瞳孔边界的曲率特征来消除这些伪影。瞳孔中心仅基于与瞳孔边界相关的点计算。对于每个边界点，计算一个曲率值。边界的遮挡在曲率函数中产生特征峰。确定了正常瞳孔大小的曲率值，并找到了一个阈值，并结合启发式方法区分了正常和异常的曲率。采用最小二乘误差准则对剩余边界点进行椭圆拟合。椭圆的中心是瞳孔中心的估计值。该方法鲁棒性好，能准确估计瞳孔中心，瞳孔边界点的可见率小于40%。
Determining the pupil center is fundamental for calculating eye orientation in video-based systems. Existing techniques are error prone and not robust because eyelids, eyelashes, corneal reflections or shadows in many instances occlude the pupil. We have developed a new algorithm which utilizes curvature characteristics of the pupil boundary to eliminate these artifacts. Pupil center is computed based solely on points related to the pupil boundary. For each boundary point, a curvature value is computed. Occlusion of the boundary induces characteristic peaks in the curvature function. Curvature values for normal pupil sizes were determined and a threshold was found which together with heuristics discriminated normal from abnormal curvature. Remaining boundary points were fit with an ellipse using a least squares error criterion. The center of the ellipse is an estimate of the pupil center. This technique is robust and accurately estimates pupil center with less than 40% of the pupil boundary points visible.