摘要
在视线跟踪过程中,变形瞳孔的定位至关重要。针对眼部图像出现干扰情况下的变形瞳孔定位,该文提出了一种基于3点的随机采样一致性定位算法RANSAC_3,即利用随机采样到的2点(以及它们的梯度方向)和搜索获得的1个点来确定椭圆参数。由于该算法在确定参数时只需随机采样两个点,从而大大降低了采样到干扰点的几率,利用搜索到的第3点来决定是否对当前点进行参数计算,有效地解决了标准随机采样一致性定位算法中的无效采样和误差累积问题,提高了椭圆拟合效率和瞳孔定位精度。实验证明,该算法对变形瞳孔具有很好的定位效果,对光斑、睫毛、头发、眼镜框以及眼球运动模糊的引起的干扰具有较强的鲁棒性,并且定位速度快,可以达到实时要求。
It is important to locate the center of distorted pupil in eye tracking process. To tackle the issues induced by distortion and disturbance, a new Random Sample Consensus algorithm based on only 3 points, named RANSAC_3, is proposed in this paper. Ellipse model is adopted to match the distorted pupil, its parameters are calculated from the two randomly sampled points and one searched point. Only two randomly sampled points are needed to estimate the ellipse parameters in this method, so the probability of sampling disturbing points is reduced. The point derived by searching can be used to determine whether to calculate the ellipse parameters, so the efficiency of ellipse fitting and the accuracy of localization are improved. Experimental results show that the method has pretty good performance on the localization of distorted pupils, and is effective and robust to various disturbances, i.e. spectrum reflection, eyelash, hair, glasses and motion blur. Moreover, its high speed ensures that it can be used in a real-time eye tracking system.
出处
《电子与信息学报》
EI
CSCD
北大核心
2010年第2期416-421,共6页
Journal of Electronics & Information Technology
基金
多媒体计算与通信教育部-微软重点实验室科研基金(07122808)
安徽省科技攻关项目(07010202046)资助课题
关键词
视线跟踪
变形瞳孔定位
随机采样一致性
最小二乘法
Eye tracking
Distorted pupil localization
Random sample consensus
Least Square Method（LSM）
