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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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An automatic improved facial expression recognition for masked faces.

Yasmeen ELsayed1, Ashraf ELSayed1,2, Mohamed A Abdou3

  • 1Faculty of Science, Alexandria University, Alexandria, Egypt.

Neural Computing & Applications
|June 5, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a hybrid deep learning model for masked facial emotion recognition. The novel approach significantly improves accuracy in detecting emotions despite face mask challenges.

Keywords:
Convolution neural networkFacial expression recognitionFeature extractionLocal binary pattern

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic facial expression recognition (AFER) is crucial for social interaction.
  • The COVID-19 pandemic and mandatory mask-wearing have posed significant challenges to AFER systems.
  • Existing machine learning techniques struggle with recognizing emotions from masked faces.

Purpose of the Study:

  • To develop an accurate AFER method for masked faces.
  • To improve the recognition of basic emotions (anger, happiness, sadness, surprise, contempt, disgust, fear) under mask conditions.

Main Methods:

  • A hybrid convolutional neural network (CNN) model was developed.
  • Local Binary Patterns (LBP) were integrated for accurate feature extraction, particularly for masked facial regions.
  • The model was evaluated on the CK, CK+, and M-LFW-FER datasets.

Main Results:

  • The proposed method achieved 70.76% accuracy in recognizing three emotions on masked faces.
  • Performance was evaluated against existing AFER techniques.
  • Significant improvements in accuracy were demonstrated compared to prior methods.

Conclusions:

  • The hybrid CNN-LBP model offers a robust solution for AFER with face masks.
  • This advancement is vital for maintaining social interaction and human-computer interaction in the post-COVID-19 era.
  • Further research can explore broader emotion sets and diverse datasets.