FFDNet:复杂环境中的细粒度面部表情识别

FFDNet:fine-grained facial expression recognition in challenging environments

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。

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.