FFDNet:复杂环境中的细粒度面部表情识别

针对面部表情识别在复杂环境中遮挡和姿态变化问题，提出一种稳健的识别模型FFDNet（feature fusion and feature decomposition net）。该算法针对人脸区域尺度的差异，采用多尺度结构进行特征融合，通过细粒度模块分解和细化特征差异，同时使用编码器捕捉具有辨别力和微小差异的特征。此外还提出一种多样性特征损失函数，驱动模型挖掘更丰富的细粒度特征。实验结果显示，FFDNet在RAF-DB和FERPlus数据集上分别获得了88.50%和88.75%的精度，同时在遮挡和姿态变化数据集上的性能都优于一些先进模型。实验结果验证了该算法的有效性。

FFDNet：fine-grained facial expression recognition in challenging environments

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.