Abstract
The conventional deep convolutional neural networks in facial expression recognition are confronted with the training inefficiency due to many layered structure with a large number of parameters, in order to cope with this challenge, in this work, an improved convolutional neural network—with parallel double channels, termed as PDC-CNN, is proposed. Within this model, we can get two different sized feature maps for the input image, and the two different sized feature maps are combined for the final recognition and judgment. In addition, in order to prevent over-fitting, we replace the traditional RuLU activation function with the Maxout model in the fully connected layer to optimize the performance of the network. We have trained and tested the new model on JAFFE dataset. Experimental results show that the proposed method can achieve 83% recognition rate, in comparison with the linear SVM, AlexNet and LeNet-5, the recognition rate of this method is improved by 14%–28%.
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
References
Takalkar, M., Xu, M., Wu, Q., Chaczko, Z.: A survey: facial micro-expression recognition. Multimedia Tools Appl. 77(15), 19301–19325 (2018)
Zhang, L., Verma, B., Tjondronegoro, D., Chandran, V.: Facial expression analysis under partial occlusion: a survey. ACM Comput. Surv. 51(2), 1557–7341 (2018)
Kumar, N., Bhargava, D.: A scheme of features fusion for facial expression analysis: a facial action recognition. J. Stat. Manage. Syst. 20(4), 693–701 (2017)
Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.R.: Real time face detection and facial expression recognition: development and applications to human computer interaction. In: 2003 Conference on Computer Vision and Pattern Recognition Workshop. IEEE (2008)
Kim, J.O., Seo, K.S., Chung, C.H., Hwang, J., Lee, W.: On facial expression recognition using the virtual image masking for a security system. Lect. Notes Comput. Sci. 3043, 655–662 (2004)
Ji, Q., Zhu, Z., Lan, P.: Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans. Veh. Technol. 53(4), 1052–1068 (2004)
Dureha, A., Dureha, A.: An accurate algorithm for generating a music playlist based on facial expressions. Int. J. Comput. Appl. 100(9), 33–39 (2014)
Collin, L., Bindra, J., Raju, M., Gillberg, C., Minnis, H.: Facial emotion recognition in child psychiatry: a systematic review. Res. Dev. Disabil. 34(5), 1505–1520 (2013)
Shan, C., Gong, S., Mcowan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
Palestra, G., Pettinicchio, A., Coco, M.D., Carcagnì, P., Leo, M., Distante, C.: Improved performance in facial expression recognition using 32 geometric features. Image Anal. Process. 9280, 518–528 (2015)
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. arXiv, 9 (2017)
Shan, C., Gong, S., Mcowan, P.W.: Robust facial expression recognition using local binary patterns. In: IEEE International Conference on Image Processing. DBLP (2005)
Li, Y., Zeng, J., Shan, S., Chen, X.: Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Trans. Image Process. 28(5), 2439–2450 (2019)
Pramerdorfer, C., Kampel, M.: Facial expression recognition using convolutional neural networks: state of the art. arXiv, 6 (2016)
Lu, G., He, J., Yan, J., Li, H.: Convolutional neural network for facial expression recognition. J. Nanjing Univ. Posts Telecommun. 36, 16–22 (2016)
Ekman, P., Friesen, W.V.: Facial action coding system (facs): a technique for the measurement of facial action. Rivista di Psichiatria 47(2), 126–138 (1978)
Torre, F.D.L., Campoy, J., Ambadar, Z., Cohn, J. F.: Temporal segmentation of facial behavior. In: IEEE International Conference on Computer Vision (2007)
Lopes, A.T., Aguiar, E.D., Oliveira-Santos, T.: A Facial expression recognition system using convolutional networks. In: 28th Sibgrapi Conference on Graphics, Patterns and Images, pp. 273–280 (2015)
Al-Shabi, M., Cheah, W.P., Connie, T.: Facial expression recognition using a hybrid CNN-SIFT aggregator. Multi disc. Trends Artif. Intell. 10607, 139–149 (2017)
Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. Comput. Sci. 28, 1319–1327 (2013)
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (61762018), the Guangxi 100 Youth Talent Program (F-KA16016) and the Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, China (GXZDSY2016-03), the research fund of Guangxi Key Lab of Multi-source Information Mining & Security (18-A-02-02), Natural Science Foundation of Guangxi (2018GXNSFAA281310).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, D.T., Jiang, F., Qin, Y.B. (2020). A New Facial Expression Recognition Scheme Based on Parallel Double Channel Convolutional Neural Network. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-42058-1_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-42057-4
Online ISBN: 978-3-030-42058-1
eBook Packages: Computer ScienceComputer Science (R0)