August 24, 2025Applied computing JournalOpen Access

Deep Transfer Learning and Feature Fusion for Improving Facial Expression Recognition on JAFFE Dataset

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The hybrid method achieved an accuracy of 96.49%, indicating significant improvement in facial expression recognition.
Using deep learning features combined with traditional descriptors, LBP and HOG improved performance on small datasets.
Data augmentation and transfer learning were employed to enhance model generalization in emotion recognition tasks.
The utility of feature fusion is demonstrated, highlighting potential for better emotion recognition systems in real-time applications.

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This approach enhances accuracy in facial expression recognition on the JAFFE dataset, suggesting benefits of feature fusion and transfer learning.

Salih et al. (2025) studied this question.

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This approach enhances accuracy in facial expression recognition on the JAFFE dataset, suggesting benefits of feature fusion and transfer learning.