Enhanced Facial Emotion Recognition and Age Estimation Using Modified Residual Network and Support Vector Machine

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This research demonstrates improved accuracy in facial emotion recognition and age estimation using image enhancement and deep learning techniques.

The model achieves 98.75–100% accuracy on the UTKFace dataset, outperforming existing methods significantly.
It shows a 5.4% increase in age estimation accuracy and a 3.75% improvement in emotion recognition, indicating enhanced performance.
Image enhancement techniques improved edge intensity and texture clarity, contributing to better classification results.
The proposed approach reduces misclassification errors by 60% for subtle emotions, validating its effectiveness in varied conditions.

El‐Hag et al. (2025) studied this question.

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This research demonstrates improved accuracy in facial emotion recognition and age estimation using image enhancement and deep learning techniques.