Open AccessFacial Expression Recognition Based on Convolutional Neural Network
Author(s) -
Ruitao Lu,
Yalan Li,
Yang Pan,
Wenfen Zhang
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 - 10.1088/1742-6596/1757/1/012100
Subject(s) - computer science , convolutional neural network , convolution (computer science) , artificial intelligence , generalization , pattern recognition (psychology) , artificial neural network , feature (linguistics) , feature extraction , deep learning , expression (computer science) , facial expression recognition , facial expression , facial recognition system , mathematics , mathematical analysis , linguistics , philosophy , programming language
At present, facial expression recognition technology is widely used in artificial intelligence, transportation, medical and other aspects, so it has important research value. Traditional facial expression recognition uses manual feature extraction method with low accuracy and weak generalization ability, which is difficult to be applied in real life. With the development of deep learning, convolution neural network appears in people’s vision. Different from traditional manual feature extraction, convolution neural network can learn image features independently, and learn more features. In addition, it has the advantage of sharing the weight with the neural network. Although convolution neural network has multiple advantages, it also has some disadvantages, especially over fitting. In this paper, the model of convolution network is improved based on the classical VGGNet according to the working principle of convolution neural network. In this new model, the number of convolution kernels is reduced in parameters, and the global average pool layer is used to replace the full connection layer in the structure, so as to reduce the degree of over fitting and decrease the operation parameters. Finally, experiments show that the accuracy, generalization and consumption of resource are enhanced in the new model. It is proposed that the new method is better than the traditional convolution network VGGNet.