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The central aim is to assess the impact of alignment and data augmentation on facial expression recognition accuracy.

Facial Expression Recognition: Compare the Role of Alignment and Augmentation

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The central aim is to assess the impact of alignment and data augmentation on facial expression recognition accuracy.
Implemented facial alignment using a 68-point landmark detection model.
Applied data augmentation techniques including scaling, rotation, and Gaussian noise addition.
Evaluated model performance using the CK+ and EXPW datasets.
All models achieved comparable accuracies around 85% on the EXPW dataset.
The aligned and augmented model did not significantly outperform others.
Model performance improved in recognizing sad expressions after augmentation, while alignment had a negligible effect.

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This analysis demonstrates improved model performance in facial expression recognition, indicating key techniques in emotion recognition.

Li et al. (2025) studied this question.

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