Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141501(2020)
Facial Expression Classification Based on Ensemble Convolutional Neural Network
In view of the high complexity of artificial feature extraction in traditional machine learning and the low recognition rate caused by inadequate feature extraction in single convolutional network, a new facial expression recognition method based on ensemble convolutional neural network is proposed. The method is to construct an ensemble network (EnsembleNet) model based on integrating an improved VGGNet-19GP model after VGGNet-19 with a ResNet-18 model. The model first trains a single model on the training set to make the single model reach the optimal experiment. Then the ensemble test is performed on the testing set. The average accuracy of 73.854% and 97.611% are obtained on FER2013 and CK+ datasets, respectively. By comparison with the VGGNet-19GP and ResNet-18 models and other existing methods, it is shown that the ensemble-based facial expression classification method has the advantages of more accurate classification and stronger generalization ability.
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Tao Zhou, Xiaoqi Lü, Guoyin Ren, Yu Gu, Ming Zhang, Jing Li. Facial Expression Classification Based on Ensemble Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141501
Category: Machine Vision
Received: Oct. 8, 2019
Accepted: Nov. 26, 2019
Published Online: Jul. 28, 2020
The Author Email: Lü Xiaoqi (lxiaoqi@imut.edu.cn)