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A facial expression recognition network using hybrid feature extraction.

Dandan Song1, Chao Liu1

  • 1Xinjiang Institute of Technology, Aksu, China.

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Summary
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This study introduces HFE-Net, a novel network for facial expression recognition. Its Hybrid Feature Extraction Block effectively captures both local and global facial cues, improving recognition accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition (FER) is challenged by face similarity, image quality, and age variation.
  • Existing Convolutional Neural Network (CNN) models struggle to capture relationships between all facial elements due to localized feature extraction.
  • This limitation hinders comprehensive understanding of complete facial expressions.

Purpose of the Study:

  • To propose a novel facial expression recognition network, HFE-Net.
  • To address the limitations of localized feature extraction in CNNs for FER.
  • To enhance the network's ability to capture subtle expression changes and holistic facial information.

Main Methods:

  • Introduced HFE-Net, featuring a Hybrid Feature Extraction Block.
  • The block combines a Feature Fusion Device for local and distant feature correlation with Multi-head Self-attention for global feature map correlation.
  • Evaluated on four public facial expression datasets.

Main Results:

  • The Hybrid Feature Extraction Block demonstrated improved facial expression recognition capabilities.
  • Experiments confirmed the effectiveness of the proposed block in enhancing feature extraction for FER.
  • HFE-Net showed superior performance in capturing both local details and global context of facial expressions.

Conclusions:

  • The proposed Hybrid Feature Extraction Block is effective for facial expression recognition.
  • HFE-Net offers a promising approach to overcome the limitations of traditional CNNs in capturing comprehensive facial expression features.
  • This method advances the field of automated facial expression analysis.