Gaze estimation reveals where a person is looking. It is an important clue for understanding human intention . The recent development of deep learning hasrevolutionized many computer vision tasks . We present a comprehensivereview of the appearance-based gaze estimation methods with deep learning. We discuss these methods from four perspectives: deep feature extraction, deep neural network architecture design, personal calibration as well as device and platform. We also survey face/eye detection method, data rectification method, 2D/3D gazeconversion 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 typicalgaze estimation algorithms. We implement these codes and set up a benchmark of the results of different methods into the same evaluation metrics. This paper not only serves as a guideline for future gaze estimation research.Implemented methods and data processing codes are available at

Author(s) : Yihua Cheng, Haofei Wang, Yiwei Bao, Feng Lu

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Keywords : estimation - gaze - deep - methods - method -

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