2024
DOI: 10.48084/etasr.9139
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Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health Detection

Mujiyanto Mujiyanto,
Arief Setyanto,
Kusrini Kusrini
et al.

Abstract: This study presents an improved Facial Expression Recognition (FER) model using Swin transformers for enhanced performance in detecting mental health through facial emotion analysis. In addition, some techniques involving better dropout and layer-wise unfreezing were implemented to reduce model overfitting. This study evaluates the proposed models on benchmark datasets such as FER2013 and CK+ and real-time Genius HR data. Model A has no dropout layer, Model B has focal loss, and Model C has enhanced dropout an… Show more

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