摘要
利用一种新的健壮的低像素的虹膜中心定位算法和眼睛角点检测算法,在摄像头像素不高的情况下,实时地提取出了注视屏幕时的眼睛特征,即虹膜中心和眼睛角点。然后利用人工神经网络(ANN),将人眼注视屏幕不同点时的眼睛特征进行分类,确定出人眼特征和屏幕上点的映射关系。这种方法只需要一个普通的商业摄像头和一般的光照条件,并且允许小范围内的头部运动,很大程度上降低了系统的硬件成本,减少了使用者的限制条件,增强了系统的实用性。实验结果表明,在普通的实验室光照条件下,这种方法在视线跟踪中达到了良好的效果。
Use a new robust and low-resolution iris center localization algorithm and eye comers detection algorithm to extract the eye features of looking at the screen in real time under low-resolution camera conditions. Then classify the eye features of looking at different positions through the Artificial Neural Network （ANN） to determine the mapping relationship of eye features and the screen points. This way only needs a commercial camera under the general illumination conditions and allows a small range of head motion, further more, it has reduced the hardware cost of the system and the restrictions of users,enhancing the system＇ s practicability. Experimental results show that in the ordinary laboratory illumination conditions, this method achieves a good accuracy in the gaze tracking.
出处
《计算机技术与发展》
2015年第4期98-101,共4页
Computer Technology and Development
基金
国家自然科学基金资助项目(61203213)
关键词
视线跟踪
人工神经网络
虹膜中心检测
眼睛角点检测
gaze tracking
artificial neural network
iris center detection
eye comers detection
