Abstract
Automatic facial expression analysis is an interesting and challenging problem; we empirically evaluate facial expression based on ensemble methodology, which builds a classification model by integrating multiple classifiers, for improving prediction performance. This method is adopted by many researchers in field of statistics, pattern recognition, and machine learning. This paper presents an ensemble-based facial expression recognition system using local binary pattern, it is used for feature extraction. The extracted feature histogram represents the local texture and global shape of face images. Three different classifiers which are Euclidian distance, neural network, and support vector machine are used for classification. The proposed ensemble classifier approach has demonstrated superior performance compared to individual classifiers. The ensemble-based classifier yielded an accuracy of 97.20 %; the best accuracy obtained from all other single classifier schemes tested using the Cohn-Kanade database.
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
Similar content being viewed by others
References
Ekman, P., & Friesen, W. (1976). Pictures of facial affect. Consulting Psychologists.
Ekman, P. (1993). Facial expression and emotion. American Psychologist, 48, 384–392.
Zaker, N., & Mahoor, M. H., Mattson, W. I., Messinger, D. S., & Cohn, J. F. (2013). A comparison of alternative classifiers for detecting occurrence and intensity in spontaneous facial expression of infants with their mothers. In Automatic Face and Gesture Recognition (pp. 22–26).
Bafandehkar, A., Nazari, M., & Rahat, M. (2011). Pictorial structure based keyparts localization for facial expression recognition using Gabor filters and Local Binary Patterns Operator. 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR) (pp. 429, 434), 14–16 Oct. 2011. doi:10.1109/SoCPaR.2011.6089282.
Kanade, T., Cohn, J. F., & Tian, Y. Comprehensive database for facial expression analysis. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France (pp. 46–53).
Ojala, T., Pietikainen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distribution. Pattern Recognition, 29(1), 51–59.
Anitha, M., Venkatesha, K., & Adiga, B. (2010). A survey on facial expression databases. International Journal of Engineering Science and Technology, 2(10), 5158–5174.
Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1).
Valstar, M., & Pantic, M. (2006). Fully automatic facial action unit detection and temporal analysis. In Proceedings of Conference on Computer Vision and Pattern Recognition (pp. 149–158).
Chen, L. S. (2000). Joint processing of audio–visual information for the recognition of emotional expressions in human–computer interaction. Ph.D. Thesis, University of Illinois at Urbana-Champaign, Department of Electrical Engineering, 2000.
Rama, L. R., Babu, G. R., & Kishore, L. (2012). Face recognition based on eigen features of multi scaled face components and artificial neural network. International Journal of Security and Its Applications (IJSIA), 5(3), SERSC, 23–44.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Jain, S., Durgesh, M., Ramesh, T. (2016). Facial Expression Recognition Using Variants of LBP and Classifier Fusion. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-0129-1_75
Download citation
DOI: https://doi.org/10.1007/978-981-10-0129-1_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0127-7
Online ISBN: 978-981-10-0129-1
eBook Packages: EngineeringEngineering (R0)