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Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation.

Min-Joo Kang1, Jung-Kyung Lee1, Je-Won Kang1

  • 1The Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea.

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|July 18, 2017
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Summary

This study introduces a novel head pose estimation method using Random Forest (RF) and texture features for improved accuracy. The technique enhances tolerance to occlusions and varying illumination conditions in facial image analysis.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Facial Image Analysis

Background:

  • Head pose estimation is crucial for human-computer interaction.
  • Existing methods struggle with occlusions and illumination variations.

Purpose of the Study:

  • To propose an efficient head pose estimation technique combining Random Forest (RF) and texture features.
  • To enhance accuracy and robustness against occlusions and illumination changes.

Main Methods:

  • Utilized a randomized tree trained with Multi-scale Block Local Block Pattern (MB-LBP) and other random features.
  • Developed a split function based on LBP uniformity for efficient node traversal.
  • Grouped independently trained trees into a Random Forest for final decision-making using Maximum-A-Posteriori criterion.

Main Results:

  • The proposed RF-based technique demonstrated significantly enhanced classification performance.
  • Achieved improved head pose estimation accuracy under diverse conditions.

Conclusions:

  • The novel technique effectively combines RF and texture features for robust head pose estimation.
  • The method shows high tolerance to variations in illumination, poses, expressions, and occlusions.