December 4, 2025International Journal of Advanced Computer Science and ApplicationsOpen Access

AI-Driven Deep Learning Architectures for Robust Emotion Recognition

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The pretrained networks achieved higher accuracy compared to custom models in classifying emotions.
VGG16 recorded 78% accuracy and 77% F1 in emotion recognition from facial expressions.
Analysis utilized dimensionality-reduction techniques and deep learning in computer vision for robust results.
Findings support the integration of efficient models in affective computing systems to address emotion recognition challenges.

Authors

HAHamad Ali Abosaq

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Implication

Analysis reveals pretrained networks enhance accuracy in emotion recognition using facial expressions, suggesting implications for affective computing systems.

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Hamad Ali Abosaq (2025) studied this question.

synapsesocial.com/papers/6930e8bdea1aef094cca33bf https://doi.org/10.14569/ijacsa.2025.0161127

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Implication

Analysis reveals pretrained networks enhance accuracy in emotion recognition using facial expressions, suggesting implications for affective computing systems.

AI-Driven Deep Learning Architectures for Robust Emotion Recognition

Key Points

Discussion

Implication

Cite This Study

AI-Driven Deep Learning Architectures for Robust Emotion Recognition

Key Points

Discussion

Implication

Cite This Study

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