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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Facial expression recognition (FER) faces challenges with real-time neutral face detection due to appearance variability.
    • Supervised methods struggle with diverse data (race, pose, lighting) and often process unnecessary neutral frames, wasting computational resources.

    Purpose of the Study:

    • To develop a lightweight pre-processing engine for facial expression recognition.
    • To improve the efficiency and accuracy of emotion recognition systems by first identifying neutral facial expressions.

    Main Methods:

    • A statistical texture model is used to dynamically learn neutral facial appearances at key emotion (KE) points.
    • The method accounts for affine distortions from head motion and uses neighborhood patch similarities for robustness.
    • It acts as a pre-processor to traditional supervised emotion classification approaches.

    Main Results:

    • The proposed engine significantly reduces computational complexity in emotion recognition systems.
    • It improves the accuracy of facial expression recognition by filtering out neutral frames.
    • The method demonstrates robustness to user head motion and dynamic shifts in key facial points.

    Conclusions:

    • The developed neutral versus emotion classification engine enhances the performance of facial expression recognition systems.
    • This approach offers a computationally efficient solution for real-time emotion analysis.
    • The method is validated on multiple databases, showing improved accuracy and reduced complexity.