  Gaze estimation reveals where a person is looking. It is an important clue
for understanding human intention. The recent development of deep learning has
revolutionized many computer vision tasks, the appearance-based gaze estimation
is no exception. However, it lacks a guideline for designing deep learning
algorithms for gaze estimation tasks. In this paper, we present a comprehensive
review of the appearance-based gaze estimation methods with deep learning. We
summarize the processing pipeline and discuss these methods from four
perspectives: deep feature extraction, deep neural network architecture design,
personal calibration as well as device and platform. Since the data
pre-processing and post-processing methods are crucial for gaze estimation, we
also survey face/eye detection method, data rectification method, 2D/3D gaze
conversion method, and gaze origin conversion method. To fairly compare the
performance of various gaze estimation approaches, we characterize all the
publicly available gaze estimation datasets and collect the code of typical
gaze estimation algorithms. We implement these codes and set up a benchmark of
converting the results of different methods into the same evaluation metrics.
This paper not only serves as a reference to develop deep learning-based gaze
estimation methods but also a guideline for future gaze estimation research.
Implemented methods and data processing codes are available at
http://phi-ai.org/GazeHub.
