Research On Facial Expression Recognition In Natural Scenes Based On Deep Learning

Facial expression is one of the easiest,most intuitive and efficient ways for humans to communicate emotional states.Facial expression recognition technology has broad application prospects in various occasions of human-computer interaction,such as social media,healthcare,traffic safety,etc.With the development of deep learning technology,the research on facial expression recognition has been transferred from the controlled scene in the laboratory to the challenging real natural scene.There are uncontrollable factors such as illumination,posture and occlusion in natural scenes,which bring great challenges to facial expression recognition tasks.In addition,problems such as inter-class similarity,intra-class diecrepancy have a prominent impact on the accuracy of model recognition.In response to the above problems,this paper aims to improve the accuracy of the facial expression recognition model in natural scenes,and conducts in-depth research on facial expression recognition algorithms in natural scenes.The main research content of the paper is as follows:Aiming at the interference of facial occlusion and head pose changes on facial expression recognition in natural scenes,a method for facial expression recognition in natural scenes based on multi-scale features and multi-head attention features is proposed.On the one hand,this method adopts a symmetrical multi-scale module,which combines facial features of different sizes of receptive fields,which can effectively reduce the sensitivity of depth convolution to facial occlusion and head pose changes;on the other hand,it uses multi-head attention The force module,which can guide the network to focus on important feature areas,alleviates the interference of facial occlusion and head posture changes on facial expression recognition.The experimental results show that the method has achieved good results on three natural scene facial expression recognition datasets,and also achieved good performance on five real face occlusion and head pose change datasets.Aiming at the problems of inter-class similarity and intra-class difference of facial expression features in natural scenes,and the ordinary convolutional neural networks cannot accurately express local features,a multi-scale cross-fusion natural scene facial expression recognition method based on Vision Transformer is proposed.The algorithm first uses the convolutional neural network to extract multi-scale facial key point features and global facial features,and then uses the Vision Transformer to cross-fuse the convolutional neural network features.The experimental results show that the method can well solve the problems of inter-class similarity and intra-class difference,etc.,and it shows good accuracy on real occlusion and pose transformation datasets,and achieve excellent performance on benchmark datasets.