Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 295-301.doi: 10.11896/jsjkx.230900094

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Eye Gaze Estimation Network Based on Class Attention

XU Jinlong1,3, DONG Mingrui1,2, LI Yingying1,3, LIU Yanqing1, HAN Lin1   

  1. 1 National Supercomputing Center in Zhengzhou,Zhengzhou 450000,China
    2 School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450000,China
    3 Information Engineering University,Zhengzhou 450000,China
  • Received:2023-09-18 Revised:2024-01-16 Online:2024-10-15 Published:2024-10-11
  • About author:XU Jinlong,born in 1985,Ph.D,master'ssupervisor.His main research interests include high performance computing and parallel compilation.
    HAN Lin,born in 1978,Ph.D,associate professor,is a senior member of CCF(No.16416M).His main research interests include compiler optimization and high performance computing.
  • Supported by:
    2022 Henan Province Major Science and Technology Special Project(2211002110600),22 Qiushi Research Initiation(Natural Science)(32213247) and 2023 Henan Province Science and Technology Research Special Project(232102210185).

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Abstract: In recent years,eye gaze estimation has attracted widespread attention.The gaze estimation method based on RGB appearance uses ordinary cameras and deep learning for gaze estimation,avoiding the use of expensive infrared devices like commercial eye trackers,providing the possibility for more accurate and cost-effective eye gaze estimation.However,due to the presence of various features unrelated to gaze,such as lighting intensity and skin color,in RGB appearance images,these irrelevant features can cause interference in the deep learning regression process,thereby affecting the accuracy of gaze estimation.In response to the above issues,this paper proposes a new architecture called class attention network(CA-Net),which includes three different class attention modules:channel,scale,and eye.Through these class attention modules,different types of attention encoding can be extracted and fused,thereby reducing the weight of gaze independent features.Extensive experiments on the GazeCapture dataset show that,compared to the state-of-the-art method,CA-Net can improve gaze estimation accuracy by approximately 0.6% and 7.4% on mobile phones and tablets,respectively,in RGB based gaze estimation methods.

Key words: Class attention, Light squeeze-and-excitation, Self-attention, Multiscale, Eye gaze estimation

CLC Number: 

  • TP183
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