| Title |
Hybrid neural networks based facial expression recognition for smart city |
| ID_Doc |
36064 |
| Authors |
Yan, LY; Sheng, MH; Wang, CZ; Gao, R; Yu, H |
| Title |
Hybrid neural networks based facial expression recognition for smart city |
| Year |
2022 |
| Published |
Multimedia Tools And Applications, 81, 1 |
| DOI |
10.1007/s11042-021-11530-7 |
| Abstract |
With the development of science and technology and the progress of human beings, intelligence is gradually integrated into human daily life. The smart city uses innovative technology to manage and operate cities intelligently. Through the research of facial expression recognition technology, this paper explores the application of facial expression recognition in smart city construction. In this paper, a hybrid neural network structure is proposed, which includes Sparse Autoencoder and Convolutional Neural Network (SCNN). The network reconstructs the input data by Sparse Autoencoder, so as to learn the approximate value between the original data and the reconstructed data, and obtain more high-dimensional abstract features. Then, combined with the Convolutional Neural Network, the features are further extracted and dimensionally reduced. The model can effectively solve the problem that the shallow network structure can not fully extract image features and train the model with a small number of samples. In this paper, CK+, FER2013 and Oulu-CASIA databases are used for Cross-Validation of the model. The experimental results show that the model has achieved good results in both databases. Compared with other methods, the accuracy of this model has been greatly improved. |
| Author Keywords |
Smart city; Facial expression recognition; Sparse Autoencoder (SAE); Convolutional Neural Network (CNN) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000694787500002 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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