Kang Suk-Ju
Department of Electronic Engineering, Sogang University, Seoul 04107, Korea.
Sensors (Basel). 2016 Dec 27;17(1):41. doi: 10.3390/s17010041.
This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB) and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F₁ score by up to 0.490, compared with benchmark algorithms.
本文提出了一种新的基于位置估计的多用户眼动跟踪算法。传统的眼动跟踪算法通常仅适用于单个用户，因此不能用于多用户系统。即使它们可用于跟踪多个用户的眼睛，但其检测精度较低，并且无法分别识别多个用户。所提出的算法解决了这些问题并提高了检测精度。具体而言，该算法采用分类器来检测红色、绿色和蓝色（RGB）图像以及深度图像中的面部。然后，基于检测到的面部区域的定向梯度直方图计算特征以识别多个用户，并从预先确定的面部数据库中选择与用户最匹配的模板。最后，该算法根据解剖比例提取最终的眼睛位置。仿真结果表明，与基准算法相比，该算法的平均F₁分数提高了多达0.490。
