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Gaze estimation reveals where a person is looking. It is an important cluefor understanding human intention. The recent development of deep learning hasrevolutionized many computer vision tasks, the appearance-based gaze estimationis no exception. However, it lacks a guideline for designing deep learningalgorithms for gaze estimation tasks. In this paper, we present a comprehensivereview of the appearance-based gaze estimation methods with deep learning. Wesummarize the processing pipeline and discuss these methods from fourperspectives: deep feature extraction, deep neural network architecture design,personal calibration as well as device and platform. Since the datapre-processing and post-processing methods are crucial for gaze estimation, wealso survey face/eye detection method, data rectification method, 2D/3D gazeconversion method, and gaze origin conversion method. To fairly compare theperformance of various gaze estimation approaches, we characterize all thepublicly available gaze estimation datasets and collect the code of typicalgaze estimation algorithms. We implement these codes and set up a benchmark ofconverting the results of different methods into the same evaluation metrics.This paper not only serves as a reference to develop deep learning-based gazeestimation methods but also a guideline for future gaze estimation research.Implemented methods and data processing codes are available athttp://phi-ai.org/GazeHub.

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark