Open AccessResearch on Facial Expression Recognition Based on Improved Deep Residual Network Model
Author(s) -
Chen Ji,
Luo Xiao-Shu,
Zhiming Meng,
Wanting Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012139
Subject(s) - residual , facial expression , weighting , computer science , artificial intelligence , convolutional neural network , expression (computer science) , facial expression recognition , pattern recognition (psychology) , facial recognition system , deep learning , face (sociological concept) , artificial neural network , face hallucination , activation function , face detection , algorithm , programming language , medicine , social science , sociology , radiology
Facial expressions are the main external manifestations of human emotions. Facial expression recognition technology can be used in medical, investigation, education and other application scenarios. Aiming at the disadvantages of slow convergence speed and low recognition accuracy of traditional neural networks, in order to optimize the network more efficiently and improve the recognition rate of facial expressions, a facial expression recognition method based on improved convolutional neural networks is proposed.First, the deep residual network ResNet18 is improved, and then the improved ResNet18 network is used to extract global expression features of face images, and then the self-attention weighting module is introduced to calculate the expression features of each face image and output one The corresponding weights are used to weight the loss function. Finally, the two public expression data sets of CK+ and RAF-DB are used to experimentally verify the method in the article. The accuracy of facial expression recognition reaches 98.89% and respectively. 87.13%, compared with the original deep residual network ResNet18 model and other types of network models, the facial expression recognition rate has been significantly improved, which proves that this method can effectively improve the accuracy of facial expression recognition and has certain application value.