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Face Image Segmentation Using Boosted Grey Wolf Optimizer.

Hongliang Zhang1, Zhennao Cai2, Lei Xiao2

  • 1Jilin Agricultural University Library, Jilin Agricultural University, Changchun 130118, China.

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|October 27, 2023
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Summary
This summary is machine-generated.

This study introduces an improved grey wolf optimizer to enhance face image segmentation using Kapur's entropy. The new method efficiently segments faces with higher accuracy and improved computational performance.

Keywords:
Kapur’s entropyface imagemeta-heuristic optimizationmulti-threshold segmentation

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image segmentation is crucial for face recognition, dividing images into regions to isolate faces.
  • Traditional threshold segmentation methods face computational challenges with increasing threshold levels.
  • Efficient and accurate face segmentation remains an active research area.

Purpose of the Study:

  • To propose an efficient multi-threshold image segmentation framework for face recognition.
  • To improve segmentation quality and threshold determination using meta-heuristic optimization.
  • To address the computational complexity issues in threshold-based segmentation.

Main Methods:

  • A novel multi-threshold image segmentation framework integrating Kapur's entropy.
  • An improved grey wolf optimizer variant for optimizing 2D Kapur's entropy.
  • Utilizing greyscale and nonlocal mean 2D histograms for image analysis.

Main Results:

  • The proposed method demonstrated superior performance compared to state-of-the-art techniques.
  • Achieved high average feature similarity (0.8792) and structural similarity (0.8532).
  • Obtained a significant average peak signal-to-noise ratio of 24.9 dB across tests.

Conclusions:

  • The developed meta-heuristic optimization approach offers an effective solution for face image segmentation.
  • The method provides a significant advancement in segmentation efficiency and accuracy.
  • This framework can serve as a valuable tool for various face recognition applications.