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Emotion Recognition for Partial Faces Using a Feature Vector Technique.

Ratanak Khoeun1, Ponlawat Chophuk1, Krisana Chinnasarn1

  • 1Faculty of Informatics, Burapha University, Chonburi 20131, Thailand.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study introduces a new method for recognizing emotions from masked faces, achieving high accuracy. The technique effectively analyzes facial features visible above the mask, outperforming existing approaches.

Keywords:
emotion recognitionfacial expressionfacial maskfeature vectorslandmark detectorocclusion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Facial emotion recognition (FER) systems struggle with performance degradation due to face masks, a common necessity during the COVID-19 pandemic.
  • Existing FER methods are often ineffective when presented with partially occluded facial images, limiting their real-world applicability.

Purpose of the Study:

  • To develop an effective feature vector technique for accurate emotion recognition from masked facial images.
  • To address the challenges posed by facial occlusions in emotion recognition tasks.

Main Methods:

  • A novel feature vector technique involving synthetic mask application, boundary and regional representation, and rapid landmark detection using an infinity shape.
  • Feature extraction utilizing landmark locations and Histograms of Oriented Gradients (HOG).
  • Classification employing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks.

Main Results:

  • The proposed method achieved high accuracy rates of 99.30% on the CK+ dataset and 95.58% on the RAF-DB dataset.
  • Demonstrated superior performance compared to existing state-of-the-art methods for masked face emotion recognition.

Conclusions:

  • The developed feature vector technique is highly effective for recognizing emotions from masked faces.
  • The approach offers a robust solution for FER systems operating in environments where face masks are prevalent.