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Improved facial emotion recognition model based on a novel deep convolutional structure.

Reham A Elsheikh1, M A Mohamed2, Ahmed Mohamed Abou-Taleb2

  • 1Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt. reham178891@gmail.com.

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Summary

This study introduces an anti-aliased deep convolution network (AA-DCN) for facial emotion recognition (FER). The AA-DCN model significantly improves emotion recognition accuracy and reduces aliasing artifacts in facial images.

Keywords:
Anti-aliasingConvolutional neural networkDeep learningEmotion recognitionFacial recognition

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial Emotion Recognition (FER) is complex due to variations in expressions, lighting, pose, and occlusions.
  • These factors degrade facial image quality, challenging accurate emotion detection.
  • Existing deep learning models often struggle with aliasing artifacts from down-sampling.

Purpose of the Study:

  • To develop and propose an anti-aliased deep convolution network (AA-DCN) model for enhanced FER.
  • To investigate the impact of anti-aliasing on improving facial emotion recognition fidelity.
  • To detect eight distinct emotions from image data using the proposed AA-DCN model.

Main Methods:

  • Developed an anti-aliased deep convolution network (AA-DCN).
  • Extracted facial emotion features using the AA-DCN and classical deep learning algorithms.
  • Evaluated the AA-DCN model on the CK+, JAFFE, and RAF datasets.

Main Results:

  • Achieved 99.26% accuracy on the CK+ dataset.
  • Obtained 98% accuracy on the JAFFE dataset.
  • Reached 82% accuracy on the challenging RAF dataset with low training time.

Conclusions:

  • The AA-DCN model significantly enhances emotion recognition accuracy.
  • Anti-aliasing effectively mitigates aliasing artifacts in deep convolution networks for FER.
  • The proposed AA-DCN demonstrates superior performance across multiple benchmark datasets.