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

作　　者：何昱均 韩永国[1] 张红英[2] He Yujun;Han Yongguo;Zhang Hongying(School of Computer Science&Technology,Southwest University of Science&Technology,Mianyang Sichuan 621000,China;School of Information Engineering,Southwest University of Science&Technology,Mianyang Sichuan 621000,China)

机构地区：[1]西南科技大学计算机科学与技术学院,四川绵阳621000 [2]西南科技大学信息工程学院,四川绵阳621000

出　　处：《计算机应用研究》2024年第5期1578-1584,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61872304)。

摘　　要：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.

关 键 词：表情识别 头部姿态 特征解耦 损失函数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]