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Attention-Guided Huber Loss for Head Pose Estimation Based on Improved Capsule Network.

Runhao Zhong1, Li He1, Hongwei Wang1

  • 1School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China.

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|July 29, 2023
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Summary

This study introduces a novel head pose estimation model using residual and capsule networks. The enhanced model improves accuracy, especially in complex scenarios with occlusions, by preserving spatial structure information.

Keywords:
capsule networkglobal attention blockhead pose estimationself-attention routing

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Head pose estimation is crucial for human behavior analysis, applied in HCI and fatigue detection.
  • Traditional methods struggle with spatial structure loss in complex scenarios like occlusions, leading to low accuracy.

Purpose of the Study:

  • To develop a robust head pose estimation model that overcomes limitations of existing methods.
  • To enhance accuracy and spatial structure preservation, particularly in challenging environments.

Main Methods:

  • A deep residual network extracts multi-level features, enhanced by a global attention block.
  • An improved capsule network with self-attention routing encodes and transmits features, preserving spatial information.
  • Optimized Huber loss is utilized for improved model robustness.

Main Results:

  • The proposed model achieves state-of-the-art performance on public datasets (300W-LP, AFLW2000, BIWI).
  • Significant improvements in accuracy were observed, especially in scenarios involving occlusions.

Conclusions:

  • The novel residual and capsule network-based approach effectively addresses spatial structure loss in head pose estimation.
  • The model demonstrates superior robustness and accuracy, particularly in complex, real-world conditions.