Open AccessResearch on Facial Expression Recognition Based on Voting Model
Author(s) -
Fei Yang,
Jun Guo
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012054
Subject(s) - voting , facial expression recognition , computer science , pattern recognition (psychology) , artificial intelligence , robustness (evolution) , facial expression , facial recognition system , classifier (uml) , majority rule , machine learning , speech recognition , biochemistry , chemistry , law , politics , political science , gene
In order to improve the recognition rate of real-time classification of facial expressions, we proposed a method of facial expression recognition based on voting mechanism. Firstly, different neural network models are constructed to learn facial features. Then, the extracted features are fed into the classifier to obtain the posterior probability of various features. Finally, through the voting mechanism, the optimal decision-making level fusion is achieved to complete the facial expression classification. Experiments show that the average recognition rate of fer2013, CK+ and JAFFE database is 74.58%, 100% and 100% respectively. Compared with other recognition methods, experiental data show that this method has superior performance, improves the recognition rate and robustness of the algorithm, and ensures the universality of the algorithm.