Human eye gaze estimation is an important cognitive ingredient for successful
human-robot interaction, enabling the robot to read and predict human behavior.
We approach this problem using artificial neural networks and build a modular
system estimating gaze from separately cropped eyes, taking advantage of
existing well-functioning components for face detection (RetinaFace) and head
pose estimation (6DRepNet). Our proposed method does not require any special
hardware or infrared filters but uses a standard notebook-builtin RGB camera,
as often approached with appearance-based methods. Using the MetaHuman tool, we
also generated a large synthetic dataset of more than 57,000 human faces and
made it publicly available. The inclusion of this dataset (with eye gaze and
head pose information) on top of the standard Columbia Gaze dataset into
training the model led to better accuracy with a mean average error below two
degrees in eye pitch and yaw directions, which compares favourably to related
methods. We also verified the feasibility of our model by its preliminary
testing in real-world setting using the builtin 4K camera in NICO semi-humanoid
robot's eye.

使用人工神经网络建立了一个模块化系统，通过对分别裁剪的眼睛进行估计，利用人脸检测和头部姿势估计组件，无需特殊硬件或红外滤光片，仅使用内置 RGB 相机进行人眼注视估计，通过包含大量合成数据集在训练模型中，达到了比相关方法更高的准确度。