Research Article
Facial Expression Recognition with Small Samples Under Convolutional Neural Network
@INPROCEEDINGS{10.1007/978-3-031-04245-4_34, author={Cheng Weiyue and Jiahao Geng and Kezheng Lin}, title={Facial Expression Recognition with Small Samples Under Convolutional Neural Network}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={Facial expression recognition CNN SIFT Small sample}, doi={10.1007/978-3-031-04245-4_34} }- Cheng Weiyue
Jiahao Geng
Kezheng Lin
Year: 2022
Facial Expression Recognition with Small Samples Under Convolutional Neural Network
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_34
Abstract
In order to further improve the accuracy of facial expression recognition in small samples, a small sample expression recognition method based on deep learning and fusion of different models is proposed. In this method, a single CNN model is first compared, and the relatively appropriate convolutional neural network (CNN) is selected by preserving probability of different nodes in the dropout layer. Then, the scale-invariant feature transformation (SIFT) algorithm is used to extract features. The purpose of extracting features with SIFT is to improve the performance of small data. And then, in order to reduce the error, avoid over fitting, all the model to carry on the summary, all the model of the weighted Average CNN-SIFT-AVG (Convolutional Neural Network and Scale Invariant Feature Transformation business) model. Finally, only a few sample data are used to train the model. The model has been tested on FER2013, CK+ and JAFFE datasets. Experimental results show that this model can greatly improve the accuracy of small sample facial expression recognition, and has produced excellent results in FER2013, CK+ and JAFFE dataset, with a maximum improvement of about 6% compared with other facial expression recognition methods.
