【摘要】 针对自然环境中存在人脸遮挡、姿势变化等复杂因素，以及卷积神经网络（CNN）中的卷积滤波器由于空间局部性无法学习大多数神经层中不同面部区域之间的长程归纳偏差的问题，提出一种用于动态人脸表情识别（DFER）的混合注意力机制模型（HA-Model），以提升DFER的鲁棒性和准确性。HA-Model由空间特征提取和时序特征处理两部分组成：空间特征提取部分通过两种注意力机制——Transformer和包含卷积块注意力模块（CBAM）的网格注意力模块，引导网络从空间角度学习含有遮挡、姿势变化的鲁棒面部特征并关注人脸局部显著特征；时序特征处理部分通过Transformer引导网络学习高层语义特征的时序联系，用于学习人脸表情特征的全局表示。实验结果表明，HA-Model在DFEW和AFEW基准上的准确率分别达到了67.27%和50.41%，验证了HA-Model可以有效提取人脸特征并提升动态人脸表情识别的精度。更多还原

【Abstract】 Complex factors such as face occlusion and pose variation exist in the wild, and the convolution filter in Convolutional Neural Network(CNN) cannot learn the long-range induction bias between different facial regions in most neural layers due to spatial locality. In order to solve the problem above, an HA-Model(Hybrid-Attention-mechanismModel) was proposed for Dynamic Facial Expression Recognition(DFER), which was used to improve the robustness and accuracy of DFER. HA-Model was composed of spatial feature extraction and temporal feature processing. Transformer and grid attention module in Convolution Block Attention Module(CBAM) in the spatial feature extraction part were used to guide the network to learn robust facial features including occlusion and pose variation from a spatial perspective, and pay attention to local significant features of the face. The temporal feature processing part was used to guide the network to learn the temporal connections of high-level semantic features through Transformer, which was used to learn the global representation of facial expression features. The experimental results show that the accuracy of HA-Model on DFEW and AFEW benchmarks reaches 67. 27% and 50. 41% respectively, which verifies that HA-Model can effectively extract facial features and improve the accuracy of DFER.更多还原