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A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications.

Hima Deepthi Vankayalapati1, Swarna Kuchibhotla2, Mohan Sai Kumar Chadalavada3

  • 1Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, India.

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Summary

This study introduces a hybrid head pose and gaze estimation (HPGE) algorithm, achieving ~85% accuracy. The method enhances human-machine interaction and driver safety systems, even under challenging conditions.

Keywords:
Zernike momentsfeature extractionhead pose and gaze estimation (HPGE)linear discriminant analysisprincipal component analysis

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Robotics

Background:

  • Real-time head pose and gaze estimation (HPGE) is crucial for advanced human-machine and human-robot interactions.
  • Applications like Driver's Assistance Systems (DAS) require accurate HPGE to prevent accidents.
  • Existing methods often struggle with variations in illumination, background, and occlusion.

Purpose of the Study:

  • To develop a novel hybrid framework for improved head pose and gaze estimation.
  • To combine appearance-based and geometric-based methods for robust feature extraction.
  • To enhance the accuracy and real-time performance of HPGE algorithms.

Main Methods:

  • A hybrid framework integrating appearance and geometric-based approaches was proposed.
  • Zernike moments were utilized for extracting rotation, scale, and illumination invariant features.
  • Conventional discriminant algorithms were employed for classifying head poses and gaze direction.

Main Results:

  • The proposed framework demonstrated accurate estimation across varying illumination conditions.
  • Achieved an accuracy of approximately 85% for head pose and gaze estimation.
  • Exhibited fast response times: 21.52 ms for head pose and 7.483 ms for gaze, independent of environmental factors.

Conclusions:

  • The hybrid HPGE algorithm offers a promising solution for robust and accurate estimation.
  • The method shows invariance to illumination, background, and occlusion, crucial for real-world applications.
  • Future developments aim for enhanced robustness, including invariance to blurring conditions.