Xiao Feng, Zheng Dandan, Huang Kejie, Qiu Yue, Shen Haibin
Institute of VLSI Design, Zhejiang University, China.
J Eye Mov Res. 2018 Oct 20;11(4). doi: 10.16910/jemr.11.4.5.
Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility. The iris center and anchor point are the most crucial factors for the accuracy of the system. The accurate iris center is detected by the simple active contour snakuscule, which is initialized by the prior knowledge of eye anatomical dimensions. After that, a novel anchor point is computed by the stable facial landmarks. Next, second-order mapping functions use the eye vectors and the head pose to estimate the points of regard. Finally, the gaze errors are improved by implementing a weight coefficient on the points of regard of the left and right eyes. The feature position of the iris center achieves an accuracy of 98.87% on the GI4E database when the normalized error is lower than 0.05. The accuracy of the gaze tracking method is superior to the-state-of-the-art appearance-based and feature- based methods on the EYEDIAP database.
注视跟踪是一种人机交互技术,在学术和工业领域都得到了广泛研究。然而,受限于特定传感器和算法的性能,它尚未普及到大众。本文提出了一种自然光下的单目注视跟踪系统,以实现其通用性。虹膜中心和锚点是系统精度的最关键因素。通过简单的主动轮廓小蛇模型检测准确的虹膜中心,该模型由眼睛解剖尺寸的先验知识初始化。之后,通过稳定的面部标志点计算出一个新的锚点。接下来,二阶映射函数利用眼睛向量和头部姿态来估计注视点。最后,通过对左右眼注视点施加权重系数来改善注视误差。当归一化误差低于0.05时,虹膜中心的特征位置在GI4E数据库上的准确率达到98.87%。在EYEDIAP数据库上,该注视跟踪方法的准确率优于基于外观和基于特征的现有最先进方法。