基于多区域检测网络的复杂场景面部表情识别

Facial Expression Recognition Under Complex Scenes Based on Multi-region Detection Network

面部表情是人类情绪状态的最直观表现,卷积神经网络在面部表情识别上表现出了优异的性能。然而复杂场景下遮挡和姿势变化仍是面部表情自动识别的两个主要问题,它们会显著改变人脸的外观,从而影响最终的识别结果。针对面部表情识别中遮挡和姿势变化的问题,提出了一种基于双注意力和多区域检测网络的面部表情识别方法。双注意力用于提升整体网络的特征提取能力,使网络能够关注更详细的特征信息。多区域检测用于在遮挡和姿态变化的面部表情识别中自适应地捕捉重要的局部区域,抑制遮挡和姿势变化带来的负面影响。最终在AffectNet、RAF-DB和SFEW三种公开的自然场景面部表情数据集上验证了所提方法的有效性。

Facial expressions are the most intuitive representation of human emotional states,and convolutional neural networks have shown excellent performance in facial expression recognition.However,occlusion and pose changes in complex scenes are still two major problems in automatic facial expression recognition,which significantly changes the appearance of faces and affects the final recognition results.Aiming at the problems of occlusion and pose change in facial expression recognition,a facial expression recognition method based on dual attention and multi-region detection network is proposed.Dual attention is used to improve the feature extraction capability of the overall network,enabling the network to focus on more detailed feature information.Multi-region detection is used to adaptively capture important local regions in facial expression recognition of occlusion and pose changes,and suppress the negative effects of occlusion and pose changes.Finally,the effectiveness of the proposed method is verified on three public natural scene facial expression datasets AffectNet,RAF-DB and SFEW.