Abstract
In order to overcome the difficulty of extracting facial expression features by neural network model, and the problems of complicated training process and parameter redundancy caused by stacking deep network structure, this paper proposes a CNN model that introduces an attention mechanism. In this paper, facial expression images are used as the object, and the research of cfacial expression recognition based on convolutional neural network is carried out. Based on the construction of natural expression feature views in the natural environment, the automatic data enhancement technology for deep convolutional neural networks is introduced, and the attention mechanism is combined to adjust the weights of different channels, and the facial expression recognition with texture information extraction as the traction is established deep learning model and facial expression recognition mechanism. At the same time, we propose a combined loss function. The effectiveness of this method is verified on the FER2013, FERplus, CK+, SFEW and RAF-DB data sets, and good results have been achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Change history
23 July 2022
The authors have retracted this conference paper because, during the pre-processing of the dataset in this study, the training set, test set and validation set were normalised together. As a result, the authors no longer have confidence in the veracity of the results and conclusions presented.
References
Wu, H.P., Lu, Z.Y., Zhang, J.F., Li, X., Zhao, M.Y., Ding, X.D.: Facial expression recognition based on multi-features cooperative deep convolutional network. Appl. Sci. 11(4), 1428 (2021)
Indira, D.N., Sumalatha, L., Markapudi, B.R.: Multi facial expression recognition (MFER) for identifying customer satisfaction on products using Deep CNN and Haar Cascade Classifier. IOP Conf. Ser. Mater. Sci. Eng. 1074(1) 012033 (2021)
Cai, Y.X., Gao, J.W., Zhang, G., Liu, Y.G.: Efficient facial expression recognition based on convolutional neural network. Intell. Data Anal. 25(1), 139–154 (2021)
Hazourli, A.R., Djeghri, A., Salam, H., Othmani, A.: Multi-facial patches aggregation network for facial expression recognition and facial regions contributions to emotion display. Multimedia Tools Appl. 80(9), 13639–13662 (2021)
Sikkandar, H., Thiyagarajan, R.: Deep learning based facial expression recognition using improved Cat Swarm Optimization. Journal of Ambient Intelligence and Humanized Computing 12(2), 3037–3053 (2020). https://doi.org/10.1007/s12652-020-02463-4
Multimedia; Findings on multimedia reported by investigators at Erciyes University. Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization. J. Eng. (2020)
Cen, S.X., Yu, Y., Yan, G., Yu, M., Yang, Q.: Sparse spatiotemporal descriptor for micro-expression recognition using enhanced local cube binary pattern. Sensors 20(16), 4437 (2020)
Bao, J., Wei, S.S., Lv, J.F., Zhang, W.L.: Optimized faster-RCNN in real-time facial expression classification. In: Proceedings of 2019 2nd International Conference on Communication, Network and Artificial Intelligence (CNAI 2019). Advanced Science and Industry Research Center, Science and Engineering Research Center, p. 8 (2019)
Hu, Q.D., Shu, Q., Bai, M.Z., Yao, X.M., Shu, K.X.: FERCaps: a capsule-based method for face expression recognition from frontal face images. In: Proceedings of 2019 International Conference on Power, Energy, Environment and Material Science (PEEMS 2019). Advanced Science and Technology Application Research Center, p. 6 (2019)
Ly, T.S., Do, N.T., Kim, S.H., Yang, H.J., Lee, G.S.: A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition. Image Vis. Comput. 92, 103817 (2019)
Signal Processing; Study Data from Guangdong University of Technology Update Understanding of Signal Processing. Occlusion expression recognition based on non-convex low-rank double dictionaries and occlusion error model. Electron. Newswkly. (2019)
Fu, Y.F., Ruan, Q.Q., Luo, Z.Y., Jin, Y., An, G.Y., Wan, J.: FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion. Signal Process. 161, 74–88 (2019)
Pollux, P.M.J., Matthew, C., Guo, K.: Gaze patterns in viewing static and dynamic body expressions. Acta Psychol. 198, 102862 (2019)
Kang, K., Ma, X.: Convolutional Gate Recurrent Unit for Video Facial Expression Recognition in the Wild, p. 6. Engineering Society of China (2019)
Michael Revina, I., Sam Emmanuel, W.R.: Face expression recognition with the optimization based multi-SVNN classifier and the modified LDP features. J. Vis. Commun. Image Represent. 62, 43–55 (2019)
Xiao, Y., Wang, D., Hou, L.: Unsupervised emotion recognition algorithm based on improved deep belief model in combination with probabilistic linear discriminant analysis. Pers. Ubiquit. Comput. 23(3–4), 553–562 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, X., Guo, Z., Duan, H., Chen, W. (2022). RETRACTED CHAPTER: An Efficient Channel Attention CNN for Facial Expression Recognition. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-6554-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6553-0
Online ISBN: 978-981-16-6554-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)