Abstract
The efficiency of facial emotion recognition (FER) systems is essential for good human–machine interaction. But the FER task is linked to several methods that influence such Systems’ performance. In our work, we study two architectures of the CNN network: VGG13 and VGG19. To examine their performances using different databases: Ck+, Fer2013, Fer+, and Fer++. In addition, we developed a new version of Fer+ to show the impact of a database with a better quality of annotations. Then we proposed a hybrid model that allows us to merge the CNN and SVM and obtain a new model that slightly enhances the performances. Our choice of combining these two architectures is motivated by the success of SVM in classification and the success of CNN in features extraction. The proposed hybrid approach is tested on CK+ Database and achieved 98.76% accuracy.
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
Plutchik, R.: Emotions and life: Perspectives from psychology, biology, and evolution. American Psychological Association (2003)
Arnold, M.B.: Emotion and Personality (1960)
Frijda, N.H.: The Laws of Emotion. Psychology Press (2017)
McDougall, W.: The nature of emotion. Psychol. Sci. Public Interest 28(3), 245 (1933)
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)
Ko, B.C.: A brief review of facial emotion recognition based on visual information. Sensors 18(2) (2018)
Suk, M., Prabhakaran, B.: Real-time mobile facial expression recognition system, a case study. IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014)
Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines. Sensors (2013)
Happy, S.L., George, A., Routray, A.: A real time facial expression classification system using local binary patterns. In: Proceedings of the 4th International Conference on Intelligent Human Computer Interaction (2012)
Walecki, R., Rudovic, O., Pavlovic, V., Schuller, B., Pantic, M.: Deep structured learning for facial action unit intensity estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings (2013)
Giusti, A., Cireşan, D.C., Masci, J., Gambardella, L. M., Schmidhuber, J.: Fast image scanning with deep max-pooling convolutional neural networks. In: 2013 IEEE International Conference on Image Processing, ICIP 2013—Proceedings (2013)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: International Conference on Engineering and Technology. ICET (2017)
Han, B., Sim, J., Adam, H.: BranchOut: Regularization for online ensemble tracking with convolutional neural networks. In: Proceedings—30th IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2017 (2017)
Fukushima, K:. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. (1980)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. et al.: Gradient-based learning applied to document recognition. IEEE (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NEURIPS (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR (2014)
He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Goodfellow, I., et al.: Challenges in representation learning: A report on three machine learning contests. In: Neural Information Processing, pp. 117–124. Springer, (2013)
Barsoum, E., Zhang, C., Canton Ferrer, C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. Microsoft Res. (2016)
Kusuma, G.P., Jonathan, J., Lim, A.P.: Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16. ASTES (2020)
Kanade, T., Cohn, J., Tian, Y.: Comprehensive database for facial expression analysis. In: International Conference on Automatic Face and Gesture Recognition (2000)
Lucey, P., et al.: The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition—Workshops, pp. 94–101. CVPRW 2010 (2010)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning. ICML (2015)
Khaireddin, Y., Chen, Z.: Facial Emotion Recognition: State of the Art Performance on FER2013. arxiv (2021)
Liu, S., Tang, X., Wang, D.: Facial Expression Recognition Based on Sobel Operator and Improved CNN-SVM. IEEE (2020)
Ahmed, I., Jeon, G., Chehri, A., Hassan, M.M.: Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies. Sustain. Cities Soc. 70 (2021)
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
Naim, S., Chaibi, H., Abdessamad, E.R., Saadane, R., Chehri, A. (2022). A Hybrid Automatic Facial Expression Recognition Based on Convolutional Neuronal Networks and Support Vector Machines Techniques. In: Zimmermann, A., Howlett, R.J., Jain, L.C. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 310. Springer, Singapore. https://doi.org/10.1007/978-981-19-3455-1_3
Download citation
DOI: https://doi.org/10.1007/978-981-19-3455-1_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3454-4
Online ISBN: 978-981-19-3455-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)