This paper proposed a robust recognition model FFDNet for facial expression recognition in complex environments with occlusion and pose variation of the face. The algorithm used a multi-scale structure for feature fusion to address the diffe-rences in face region scales. It decomposed feature differences and fine-grained by fine-grained modules, and used encoders to capture features with discriminative power and small differences. Furthermore it proposed a diversity feature loss function to drive the model to mine richer fine-grained features. Experimental results show that FFDNet obtains 88.50% and 88.75% accuracy on the RAF-DB and FERPlus datasets, respectively, while outperforming some state-of-the-art models on both occlusion and pose variation datasets. The experimental results demonstrate the effectiveness of the algorithm.

[2023-11-01] Accepted Paper /doc/2023.08.0394/v1

[2024-05-05] Printed Article /doc/2023.08.0394/v2