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Graph based feature extraction and hybrid classification approach for facial expression recognition.

L B Krithika1, G G Lakshmi Priya1

  • 1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.

Journal of Ambient Intelligence and Humanized Computing
|August 25, 2020
PubMed
Summary

This study introduces a novel Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) for accurate human emotion recognition. The GFE-HCA method significantly improves facial expression recognition rates compared to existing techniques.

Keywords:
Emotion recognitionFacial expressionSelf-organizing map based neural networkWeighted visibility graph

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Existing algorithms often suffer from inaccurate facial expression recognition.

Purpose of the Study:

  • To propose an effective Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) for accurate human emotion recognition.
  • To overcome limitations of current facial expression recognition methods.

Main Methods:

  • Face detection using the Viola-Jones algorithm.
  • Extraction of facial parts (eyes, nose, mouth) and edge-based invariant features.
  • Optimization of features using Weighted Visibility Graph for graph-based features.
  • Classification using a Self-Organizing Map based Neural Network Classifier.

Main Results:

  • The proposed GFE-HCA approach demonstrated superior performance in facial expression recognition.
  • Achieved a higher recognition rate compared to existing techniques.

Conclusions:

  • The GFE-HCA approach is effective for accurate human emotion recognition.
  • The method offers significant improvements over traditional facial expression recognition techniques.