基于遮挡感知卷积神经网络的面部表情识别模型

针对面部遮挡情况下表情特征难以提取的问题，提出一种双通道遮挡感知神经网络模型。设计区域遮挡判定单元并集成到VGG16网络中形成遮挡感知神经网络，提取面部图像中未遮挡区域及遮挡较少区域的表情特征。运用迁移学习算法对卷积层参数进行预训练，减轻训练数据样本不足带来的过拟合问题。通过优化残差网络提取全脸表情相关特征，在此基础上加权融合遮挡感知神经网络和残差网络的输出以识别表情。在CK+、RAF-DB、SFEW这3个公开数据库上进行对比实验，结果表明，该模型平均准确率分别达到97.33%、86%、61.06%，与OPCNN、ResNet、VGG16等传统卷积神经网络模型相比，有效提高了面部遮挡情况下的表情识别精度。

Facial Expression Recognition Model Based on Convolutional Neural Network with Occlusion Perception

To reduce the difficulty in extracting features of an occluded face, a dual-channel Convolutional Neural Network (CNN) model with occlusion perception is proposed.The model is constructed by integrating newly designed occlusiondecision units into VGG16 network, which aims at extractingexpression-related features of the areas that are less occluded.The model employs the transfer learning algorithm to pre-train the parameters of the convolutional layer, which means to alleviate the over-fittingproblem.At the meantime, the expression-related features of the whole facial image are extracted by the modified residual network.Finally, the outputs of theperceptive neural network and residual network arefused in a weighted manner.The experimental results show that the proposed model achieves an accuracy of 97.33% on CK+, 86% on RAF-DB, and 61.06%on SFEW.Compared with traditional OPCNN, ResNet, and VGG16 models, the proposed model exhibits a significant improvement in the accuracy of recognizing the expression of an occluded face.