Facial Expression Recognition Based on RS-SVM

Dong Li

doi:10.4028/www.scientific.net/AMM.543-547.2329

Paper Titles

Method of Coal-Rock Boundary Recognition Based on Modal Analysis
p.2308

Research on AOD of Shandong Province Based on MODIS Data
p.2312

High Resolution RS Image Industrial Solid Wastes Extraction Based on SVM
p.2318

A Method of Constructing Quaternary Periodic Complementary Sequence Sets for Suppression of Multiple Access Interference in CDMA Communication Systems
p.2323

Facial Expression Recognition Based on RS-SVM
p.2329

Comparison between BP and RBF Neural Network Pattern Recognition Process Applied in the Droplet Analyzer
p.2333

A Recognition Judgment Method of Isolated-Word Speech-Recognition
p.2337

Acquisition Algorithm Based on Circular Correlation for GPS L2C CM Code Signal and the Software Implementation
p.2341

A Variable Mode QR Decomposition Adaptive Filtering Algorithm for Acoustic Echo Cancellation
p.2345

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Facial Expression Recognition Based on RS-SVM

Facial Expression Recognition Based on RS-SVM

Abstract:

In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

2329-2332

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.2329

Citation:

Cite this paper

Online since:

March 2014

Authors:

Dong Li*

Keywords:

Classification, Facial Expression Recognition, Rough Set (RS), Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J.M. Buenaposa, E. Muoz, L. Baumela, Recognising facial expressions in video sequences, Pattern Analysis and Applications, pp.101-116, (2008).

DOI: 10.1007/s10044-007-0084-8

Google Scholar

[2] P. Michel, R. El Kaliouby, Real time facial expression recognition in video using suport vector machines, In Porc. Int. Conf. on Multimodal Interfaces, ACM, pp.258-264, (2003).

DOI: 10.1145/958432.958479

Google Scholar

[3] G. Zhao, M Piettikinen, Dynamic texture recognition using local binary patters with an application to facial expressions, IEEE Trans. PAMI, 29(6): pp.915-928, (2007).

DOI: 10.1109/tpami.2007.1110

Google Scholar

[4] M. Kudo, J. Sklansky, Comparison of Algorithms that Select Features for Pattern Classifiers. Pattern Recognit, 33 (1), p.25–41, (2000).

DOI: 10.1016/s0031-3203(99)00041-2

Google Scholar

[5] N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge, Cambridge University Press, (2000).

Google Scholar