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Gaze Estimation Approach Using Deep Differential Residual Network.

Longzhao Huang1, Yujie Li1, Xu Wang1

  • 1School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China.

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|July 27, 2022
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Summary
This summary is machine-generated.

Differential residual models improve gaze estimation by utilizing eye differences. DRNet enhances accuracy and robustness, outperforming current methods on public datasets.

Keywords:
differential residual networkgaze calibrationgaze estimationnoise image

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Gaze estimation, crucial for understanding human intention, benefits from deep learning (DL) methods.
  • Existing DL gaze estimation techniques face challenges with calibration, limiting performance improvements.
  • Differential networks (Diff-Nn) offer a solution by predicting eye differences but suffer accuracy loss with single images.

Purpose of the Study:

  • To propose a novel differential residual model (DRNet) for gaze estimation.
  • To leverage the difference information between two eye images as auxiliary data.
  • To introduce a new loss function to enhance the utilization of differential eye features.

Main Methods:

  • Developed a differential residual model (DRNet) incorporating a novel loss function.
  • Trained and evaluated DRNet using the MpiiGaze and Eyediap public datasets.
  • Focused on utilizing only eye features for gaze estimation.

Main Results:

  • DRNet achieved superior performance compared to state-of-the-art methods on both MpiiGaze (4.57° angular error) and Eyediap (6.14° angular error).
  • The model demonstrated significant robustness against noise in images.
  • Differential information proved effective as auxiliary data for gaze estimation.

Conclusions:

  • DRNet effectively utilizes differential eye information, outperforming existing gaze estimation models.
  • The proposed method offers enhanced accuracy and robustness, addressing limitations of previous approaches.
  • DRNet represents a significant advancement in gaze estimation technology, particularly for noisy image conditions.