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Appearance-Based Gaze Estimation Method Using Static Transformer Temporal Differential Network

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  • Yujie Li

    (Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering, School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China)

  • Longzhao Huang

    (School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China)

  • Jiahui Chen

    (School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China)

  • Xiwen Wang

    (School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China)

  • Benying Tan

    (Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering, School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China)

Abstract

Gaze behavior is important and non-invasive human–computer interaction information that plays an important role in many fields—including skills transfer, psychology, and human–computer interaction. Recently, improving the performance of appearance-based gaze estimation, using deep learning techniques, has attracted increasing attention: however, several key problems in these deep-learning-based gaze estimation methods remain. Firstly, the feature fusion stage is not fully considered: existing methods simply concatenate the different obtained features into one feature, without considering their internal relationship. Secondly, dynamic features can be difficult to learn, because of the unstable extraction process of ambiguously defined dynamic features. In this study, we propose a novel method to consider feature fusion and dynamic feature extraction problems. We propose the static transformer module (STM), which uses a multi-head self-attention mechanism to fuse fine-grained eye features and coarse-grained facial features. Additionally, we propose an innovative recurrent neural network (RNN) cell—that is, the temporal differential module (TDM)—which can be used to extract dynamic features. We integrated the STM and the TDM into the static transformer with a temporal differential network (STTDN). We evaluated the STTDN performance, using two publicly available datasets (MPIIFaceGaze and Eyediap), and demonstrated the effectiveness of the STM and the TDM. Our results show that the proposed STTDN outperformed state-of-the-art methods, including that of Eyediap (by 2.9%).

Suggested Citation

  • Yujie Li & Longzhao Huang & Jiahui Chen & Xiwen Wang & Benying Tan, 2023. "Appearance-Based Gaze Estimation Method Using Static Transformer Temporal Differential Network," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:686-:d:1050410
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    References listed on IDEAS

    as
    1. Baoguo Xu & Wenlong Li & Deping Liu & Kun Zhang & Minmin Miao & Guozheng Xu & Aiguo Song, 2022. "Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    2. Pieter Vanneste & José Oramas & Thomas Verelst & Tinne Tuytelaars & Annelies Raes & Fien Depaepe & Wim Van den Noortgate, 2021. "Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement," Mathematics, MDPI, vol. 9(3), pages 1-20, February.
    3. Hao Ma & Wenhui Pei & Qi Zhang, 2022. "Research on Path Planning Algorithm for Driverless Vehicles," Mathematics, MDPI, vol. 10(15), pages 1-14, July.
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