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Facial Expression Recognition: One Attention-Modulated Contextual Spatial Information Network.

Xue Li1,2,3, Chunhua Zhu1,2,3, Fei Zhou1,2,3

  • 1College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces ACSI-Net, a novel model for facial expression recognition (FER) in the wild. It effectively captures long-range dependencies and refines subtle expression distinctions for improved accuracy.

Keywords:
deep learningfacial expression recognitionfeatures extractionneural networkspatial information

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial Expression Recognition (FER) in uncontrolled environments (in the wild) is hindered by factors like occlusion, illumination, and pose variations.
  • Existing FER methods struggle with capturing long-range dependencies in facial features and distinguishing subtle, varied expressions.

Purpose of the Study:

  • To develop an end-to-end model, ACSI-Net, that addresses the limitations of current in-the-wild FER approaches.
  • To enhance the model's ability to describe long-range dependencies and refine inter-class distinctions for diverse facial expressions.

Main Methods:

  • An attention-modulated contextual spatial information network (ACSI-Net) was designed, integrating coordinate attention (CA) modules within a contextual convolutional residual network (CoResNet).
  • CoResNet utilizes contextual convolution (CoConv) blocks to capture long-range dependencies and holistic spatial information.
  • CA modules are embedded at various stages to modulate subtle expression features and a final CA module generates attention masks to focus on salient facial regions.

Main Results:

  • ACSI-Net demonstrates superior performance in exploring intrinsic feature dependencies and generating discriminative representations for facial expression classification.
  • Experiments on AffectNet and RAF_DB datasets validate the model's effectiveness and competitiveness against existing FER methods.

Conclusions:

  • ACSI-Net offers a robust solution for in-the-wild facial expression recognition by effectively integrating contextual spatial information and attention mechanisms.
  • The proposed model significantly advances the state-of-the-art in recognizing complex facial expressions under challenging real-world conditions.