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A Cross-Modal Network for Facial Expression Recognition.

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    This study introduces CMNet, a novel cross-modal network for facial expression recognition. CMNet effectively utilizes face symmetry and complementary features for improved accuracy in recognizing human emotions.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Deep neural networks are common for facial expression recognition but often miss crucial face properties.
    • Existing methods may rely on hierarchical information, limiting their ability to capture holistic facial cues.

    Purpose of the Study:

    • To propose a novel cross-modal network (CMNet) for enhanced facial expression recognition.
    • To leverage biological and structural information, focusing on face symmetry for feature extraction.

    Main Methods:

    • CMNet learns expression information from whole, left, and right half faces using symmetry.
    • A salient facial information refinement module enhances classifier stability.
    • A half-face alignment optimization mechanism ensures balanced feature learning.

    Main Results:

    • CMNet demonstrated superior performance compared to state-of-the-art methods like SCN and LAENet-SA.
    • The proposed network effectively extracts complementary facial features through symmetry analysis.
    • Experimental validation confirmed the robustness and accuracy of CMNet.

    Conclusions:

    • CMNet offers a significant advancement in facial expression recognition by integrating structural and biological information.
    • The network's ability to learn from face symmetry and refine salient features improves recognition stability and accuracy.
    • The findings suggest a promising direction for developing more sophisticated emotion recognition systems.