Research on Facial Expression Recognition Method Combined with Improved ResNet18 Network Model

Abstract

AbstractAt present, facial expression recognition is widely used and is an important part of human-computer interaction. With the rapid development of deep learning, facial expression recognition technology will be more widely used. However, when facial expression recognition technology is applied to large-scale facial expression database, the accuracy of facial expression recognition is low and it is easy to produce defects such as over fitting. In order to optimize the deep learning network and further improve the accuracy of facial expression recognition, this paper proposes an improved network model combined with resnet18. The model replaces the relu activation function after the accumulation layer of the backbone network with the mish activation function. After the feature extraction of facial expression pictures, softpool is used to improve the information loss caused by down sampling, which is conducive to distinguishing similar key points, So as to improve the accuracy of facial expression recognition. The improved network model is verified on raf-db, CK + and Jaffe data sets, and the human face expression recognition rates reach 87.54%, 99.9% and 99.5% respectively, which is better than the face expression recognition of the original resnet18 network model. It is proved that this method is effective and valuable for face expression recognition.KeywordsExpression recognitionDeep learningMish functionFeature extractionSoftpool pooling