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

• Published in: IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.7509-7528
• Main Authors: Cheng, Yihua, Wang, Haofei, Bao, Yiwei, Lu, Feng
• Format: Article
• Language: English
• Subjects: Benchmark; Calibration; Cameras; Deep learning; Estimation; eye appearance; Feature extraction; gaze estimation; review; Solid modeling; Three-dimensional displays
• ISSN: 0162-8828; 1939-3539; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2024.3393571

Human gaze provides valuable information on human focus and intentions, making it a crucial area of research. Recently, deep learning has revolutionized appearance-based gaze estimation. However, due to the unique features of gaze estimation research, such as the unfair comparison between 2D gaze positions and 3D gaze vectors and the different pre-processing and post-processing methods, there is a lack of a definitive guideline for developing deep learning-based gaze estimation algorithms. In this paper, we present a systematic review of the appearance-based gaze estimation methods using deep learning. First, we survey the existing gaze estimation algorithms along the typical gaze estimation pipeline: deep feature extraction, deep learning model design, personal calibration and platforms. Second, to fairly compare the performance of different approaches, we summarize the data pre-processing and post-processing methods, including face/eye detection, data rectification, 2D/3D gaze conversion and gaze origin conversion. Finally, we set up a comprehensive benchmark for deep learning-based gaze estimation. We characterize all the public datasets and provide the source code of typical gaze estimation algorithms. This paper serves not only as a reference to develop deep learning-based gaze estimation methods, but also a guideline for future gaze estimation research.