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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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Enhancing Emotion Recognition: A Dual-Input Model for Facial Expression Recognition Using Images and Facial

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    This study enhances automatic facial expression recognition by fusing two neural networks. The combined approach achieved 60.17% accuracy on the AffectNet dataset, matching top performance for emotion recognition.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Facial expressions are key to human nonverbal communication and emotion conveyance.
    • Subjective assessment challenges facial expression recognition databases due to potential bias.
    • Deep learning and image processing significantly advance facial expression recognition algorithms.

    Purpose of the Study:

    • To improve automatic facial expression recognition accuracy.
    • To present a novel fusion of two neural network architectures for enhanced performance.

    Main Methods:

    • A hybrid neural network architecture was developed, combining a 1D convolutional neural network (CNN) with facial landmarks and a DenseNet-based CNN using face images.
    • The ADAM optimizer was utilized for network training.
    • The AffectNet database was used for evaluation.

    Main Results:

    • The fused network achieved a test accuracy of 60.17% for 7 emotion classes.
    • This performance is comparable to state-of-the-art results on the AffectNet dataset.

    Conclusions:

    • The fusion of 1D CNN with facial landmarks and DenseNet-based CNN shows promise for improving automatic facial expression recognition.
    • The proposed method achieves competitive accuracy, demonstrating the effectiveness of combining different neural network approaches.