Leveraging Efficient Models for Recognizing Drivers' Facial Expressions
Résumé
Our new approach, ShuffViT-DFER, combines lightweight CNN and vision transformer models for efficient and accurate real-time driver facial expression recognition. By merging their features effectively, we outperform existing methods on benchmark datasets like KMU-FED and KDEF.
| Origine | Fichiers produits par l'(les) auteur(s) |
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