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Facial expression recognition using lightweight deep learning modeling.

Mubashir Ahmad1,2, Saira Sanawar2, Omar Alfandi3

  • 1Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Tobe Camp, Abbottabad-22060, Pakistan.

Mathematical Biosciences and Engineering : MBE
|May 10, 2023
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Summary

This study introduces a deep learning method for facial expression recognition, achieving high accuracy in classifying emotions like happiness and anger. The approach effectively handles image complexities for improved computer vision applications.

Keywords:
classificationdeep learningfacial expression recognitionmachine learningstacked sparse auto-encoder

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is vital for human-computer interaction and behavior analysis.
  • Challenges include variations in illumination, occlusion, and noise, alongside the overfitting problem.
  • Accurate classification of human facial expressions remains a critical task in image processing.

Purpose of the Study:

  • To present a deep learning approach for classifying seven basic human facial expressions.
  • To address the complexities and inconsistencies in facial expression detection and classification.
  • To introduce a novel method for automatic feature extraction and expression classification.

Main Methods:

  • A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) was utilized.
  • The method involved unsupervised pre-training and supervised fine-tuning of the SSAE-FER.
  • Automatic feature extraction was performed by SSAE-FER, followed by softmax classification.

Main Results:

  • The SSAE-FER method achieved 92.50% accuracy on the JAFFE dataset.
  • The SSAE-FER method achieved 99.30% accuracy on the CK+ dataset.
  • The proposed method demonstrated superior performance compared to existing comparative methods.

Conclusions:

  • The SSAE-FER deep learning approach effectively recognizes human facial expressions.
  • The method shows high accuracy and robustness in handling complex image conditions.
  • This technique offers a promising advancement for intelligent visual surveillance and human-robot interaction.