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Appearance-Based Gaze Tracking: A Brief Review

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Intelligent Robotics and Applications (ICIRA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11745))

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Abstract

Human gaze tracking plays an important role in the field of Human-Computer Interaction. This paper presents a brief review on appearance-based gaze tracking. Based on the appearance of human eyes, input features can be classified into three categories according to the different ways of extracting human eyes features, namely, complete human eye image, pixel-based feature and 3D reconstruction image. The estimation process from human eye feature to fixation point mainly uses different mapping functions. In this paper, common mapping functions and related algorithms are described in detail: k-nearest neighbor (KNN), random forest (RF) regression, gaussian process (GP) regression, support vector machines (SVM) and artificial neural networks (ANN). This paper evaluates the performance of these gaze tracking algorithms using different mapping functions. Based on the results of the evaluation, potential challenges are summarized and the future directions of gaze estimation are prospected.

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Acknowledgement

This work was supported by National Natural Science Foundation of China (61876168, U1509207), National Key R&D Program of China (2018YFB1305200), and Zhejiang Provincial Natural Science Foundation of China (LY18F030020).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Jiaqi Jiang, Xiaolong Zhou, Sixian Chan & Shengyong Chen

  2. College of Electrical and Information Engineering, Quzhou University, Quzhou, China

    Xiaolong Zhou

  3. School of Computer Communication and Engineering, Tianjin University of Technology, Tianjin, China

    Shengyong Chen

Authors
  1. Jiaqi Jiang
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  2. Xiaolong Zhou
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  3. Sixian Chan
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  4. Shengyong Chen
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Corresponding author

Correspondence to Xiaolong Zhou .

Editor information

Editors and Affiliations

  1. Shenyang Institute of Automation, Shenyang, China

    Haibin Yu

  2. Shenyang Institute of Automation, Shenyang, China

    Jinguo Liu

  3. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  4. University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  5. Shenyang Institute of Automation, Shenyang, China

    Yuwang Liu

  6. University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Jiang, J., Zhou, X., Chan, S., Chen, S. (2019). Appearance-Based Gaze Tracking: A Brief Review. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_53

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  • DOI: https://doi.org/10.1007/978-3-030-27529-7_53

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  • Online ISBN: 978-3-030-27529-7

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