AI-Driven Deep Learning Architectures for Robust Emotion Recognition

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Emotion recognition accuracy improved with pretrained networks in facial expressions classification, addressing previous challenges.
The custom CNN achieved 66% accuracy, while VGG16 and MobileNetV performed better with 78% and 77% accuracy, respectively.
Assessment using paired t-tests confirmed the superiority of pretrained models over custom models in emotion classification.
The findings highlight the potential of lightweight models in affective computing applications, reducing the trade-off between accuracy and generalization.

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This analysis shows improvements in facial expression classification using deep learning models, suggesting pretrained networks enhance accuracy.

Hamad Ali Abosaq (2025) studied this question.

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This analysis shows improvements in facial expression classification using deep learning models, suggesting pretrained networks enhance accuracy.