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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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Facial Emotion Recognition Focused on Descriptive Region Segmentation.

H Arabian, V Wagner-Hartl, J Geoffrey Chase

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    |December 11, 2021
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    Summary
    This summary is machine-generated.

    This study developed a real-time facial emotion recognition (FER) system using machine learning. The system accurately identifies emotions by focusing on key facial regions, benefiting children with Autism Spectrum Disorder (ASD).

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    Area of Science:

    • Computer Science
    • Psychology
    • Biomedical Engineering

    Background:

    • Facial emotion recognition (FER) is crucial for social interaction.
    • Children with Autism Spectrum Disorder (ASD) often experience challenges in recognizing facial expressions.
    • Automated FER systems can provide valuable feedback for emotion recognition training.

    Purpose of the Study:

    • To explore the potential of real-time FER using local regions of interest and machine learning.
    • To develop and evaluate a FER system for assisting in emotion recognition training for children with ASD.

    Main Methods:

    • Utilized Histogram of Oriented Gradients (HOG) for feature extraction.
    • Implemented k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers.
    • Compared model performance using accuracy on randomly selected validation sets from the Oulu-CASIA database.

    Main Results:

    • Achieved accuracies up to 98.44% with minimal variation based on data distribution.
    • Demonstrated that focusing on smaller, informative facial regions is as effective as using the entire image.
    • The region selection methodology balanced accuracy with the number of extracted features.

    Conclusions:

    • Real-time FER focusing on local regions of interest is a viable approach.
    • This method shows significant promise for developing feedback systems for children with ASD.
    • The study validates the effectiveness of targeted feature extraction in FER.