The week's pick
Random Articles
Reseach Article
Facial Expression Recognition in Video using Adaboost and SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 104 - Number 2 |
| Year of Publication: 2014 |
| Authors: Surabhi Prabhakar, Jaya Sharma, Shilpi Gupta |
10.5120/18171-9055
|
Surabhi Prabhakar, Jaya Sharma, Shilpi Gupta . Facial Expression Recognition in Video using Adaboost and SVM. International Journal of Computer Applications. 104, 2 ( October 2014), 1-4. DOI=10.5120/18171-9055
Abstract
In human-computer interaction facial expression is the characteristic and proficient method for correspondence, and has been acknowledged as essential input of such interface. In this paper, we present an enhancement in facial expression recognition for image sequence. The most important step is to extract essential features from face to efficiently determine facial expression. Experimentation shows that LBP method performs well while extracting facial features. We further found that Boosted-LBP extracts most distinct features and the best recognition is calculated by SVM classifier.
References
- F. De la Torre and J. F. Cohn, "Guide to visual analysis of humans: looking at people, chapter Facial Expression Analysis, Springer, 2011.
- T. Kanade, J. F. Cohn, and Y. Tian. Comprehensive database for facial expression analysis. IEEE, pages 46–53, Grenoble, France,
- T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002.
- Y. Tian, Evaluation of face resolution for expression analysis, in: CVPR Workshop on Face Processing in Video, 2004. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
- P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
- T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distribution, Pattern Recognition, 1996.
- Y. Freund, R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences ,1997.
- R. E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, Maching Learning, 1999.
- S. Har-Peled, D. Roth, D. Zimak, Constraint classification for multiclass classification and ranking, in: S. Becker, K. Obermayer (Eds. ), Advances in Neural Information Processing Systems, vol. 15, MIT Press,Cambridge, MA, 2003.
- J. Freund, R. E. Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence 14 (5) (1999) 771–780.
- J. Friedman, T. Hastie, R. Tibshirani, Additive Logistic Regression: A Statistical View of Boosting, 1998.
- G. Zhao, M. Pietikäinen, Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (6) (2007) 915–928.
Index Terms
Keywords