Research Article

Texture based Emotion Recognition from Facial Expressions using Support Vector Machine

by  A. Punitha, M. Kalaiselvi Geetha
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Issue 5
Published: October 2013
Authors: A. Punitha, M. Kalaiselvi Geetha
10.5120/13854-1715
PDF

A. Punitha, M. Kalaiselvi Geetha . Texture based Emotion Recognition from Facial Expressions using Support Vector Machine. International Journal of Computer Applications. 80, 5 (October 2013), 1-5. DOI=10.5120/13854-1715

                        @article{ 10.5120/13854-1715,
                        author  = { A. Punitha,M. Kalaiselvi Geetha },
                        title   = { Texture based Emotion Recognition from Facial Expressions using Support Vector Machine },
                        journal = { International Journal of Computer Applications },
                        year    = { 2013 },
                        volume  = { 80 },
                        number  = { 5 },
                        pages   = { 1-5 },
                        doi     = { 10.5120/13854-1715 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A A. Punitha
                        %A M. Kalaiselvi Geetha
                        %T Texture based Emotion Recognition from Facial Expressions using Support Vector Machine%T 
                        %J International Journal of Computer Applications
                        %V 80
                        %N 5
                        %P 1-5
                        %R 10.5120/13854-1715
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

The mission of automatically recognizing different facial expressions in human-computer environment is significant and challenging. This paper presents a method to identify the facial expressions by processing images taken from Facial Expression Database. The approach for emotion recognition is based on the texture features extracted from the gray-level co-occurrence matrix(GLCM) . The results show that the features are highly efficient to discriminate the expressions and require less computation time. The extracted GLCM features are trained with Support Vector Machine using different kernels to recognize the basic emotions Happy, Disgust, Surprise and Neutral.

References
  • Gokturk, S. B. , Bouguet, J. Y. , Tomasi, C. and Girod, B. , "Model-based face tracking for view independent facial expression recognition, ", Proc. IEEE International Conference in Face and Gesture Recognition,,2002, pp. 272-278.
  • Chang, Y. , Hu, C. , Feris, R. and Turk, M. , "Manifold based analysis of facial expression ", Journal of Image and Vision Computing,, 2006, Vol. 24, No. 6, pp. 605-614.
  • Guo, G. and Dyer, C. R. , "Learning From Examples in the Small Sample Case - Face Expression Recognition. ", IEEE Trans. Systems, Man, and Cybernetics - Part B , Vol. 35, No. 3, pp. 477–488.
  • Bartlett, M. S. , Littlewort, G. , Frank, M. G. , Lainscsek, C. , Fasel, I. and Movellan, J. , "Fully automatic facial action recognition in spontaneous behavior ", Proc. IEEE Conf. Automatic Face and Gesture Recognition , 2006, pp. 223–230.
  • Anderson, K. and McOwan, P. W. , "A Real-Time Automated System for Recognition of Human Facial Expressions ", IEEE Trans. Systems, Man, and Cybernetics - Part B, 2006, Vol. 36, No. 1, pp. 96-105.
  • Lienhart, R. , Fasel, B. , and Luettin, J, "Automatic facial expression analysis: a survey", Pattern Recognition , 2003, pp. 259–275.
  • Pantic, M. , and Rothkrantz, L, "Automatic analysis of facial expressions: the state of art", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, Vol. 22 (12) , pp. 1424–1445.
  • S. -S. Liu, Y. -T. Tian, and D. Li, "New Research Advances of Facial Expression Recognition", International Conference on Machine on Machine Learning and Cybernetics,Baoding, 2009, Vol. 2, pp. 1150–1151.
  • Freedman, D. , "Active Contours for Tracking Distributions", IEEE Transactions on Image Processing, 2003, Vol. 13, No. 4, pp. 518–526.
  • Nguyen, H. T. , and Smeulders, A. W. M. , "Fast Occluded Object Tracking by a Robust Appearance Filter", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, Vol. 26, No. 8, pp 1099–1104.
  • Batur, A. U. , and Hayes, M. H. , " Adaptive Active Appearance Models", IEEE Transactions on Image Processing, 2005, Vol. 14, No. 11, pp. 1707–1721.
  • Corcoran, P. , Ionita, M. C. , and Bacivarov, I. , " Next Generation Face Tracking Technology Using AAM TechniquesA", Proceedings of International Symposium on Signals, Systems and Circuits, 2007, Vol. 1, pp. 1–4.
  • Gyanendra. K. Verma, Tiwary V. S, and Mahendra. K. Rai, " Facial Emotion REcognition using Different Muti Resoluiton Transforms", Advances in Computing and Communication, 2011, Vol. 192, pp. 469–477.
  • Reena Rose, R. , and Suruliandi, A. , " Improving Performance of Texture Based Face Recognition Systems by Segmenting Face Region", ACEEE Int. Journal. on Network Security , 2011, Vol. 02, No. 03.
  • Guoying Zhao and Matti Pietikainen, "Dynamic Texture Recognition using Local Binary Patterns with an Application to Facial Expressions", IEEE transactions on Pattern Analysis and Machine Intelligence, 2007.
  • Mohanta, P. , P, Saha. S. K, ChandaPardàs, M. , Bonafonte, A. and Landabaso, J, B, "Emotion recognition based on MPEG4 facial animation parameters, " Proceedings of IEEE ICASSP, 2002.
  • Cohen, I. , Sebe, N. , Cozman, f. , Cirelo, M. , and Huang, T. , "Learning Bayesian network classifiers for facial expression recognition using both labelled and unlabeled data", Proceedings of the 2003 IEEE CVPR, 2003.
  • Susskind, J. M. , Littlewort, G. , Bartlett, M. S. , Movellan, J. , and Anderson, A. K. , " Human and computer recognition of facial expressions of emotion, Neuro psychologia", 2007, Vol. 45, pp. 152-162.
  • Geetha, A. , Ramalingam, V. and Palanivel, S. , "Facial expression recognition, A real time approach", Expert Systems with Applications, 2009, Vol. 36, pp. 303-308.
  • Haralick, R. M. , Shanmugam, K. , ItsŠ hak Dinstein, " Texture features for image classification", IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6), pp. 610–621.
  • Hong, Q. Q. , and Wang, B. Z. , " An improved approach for image retrieval based on generalized co-occurrence matrix", Journal of Computational Information Systems, 2008, 4: 1, pp. 97–104.
  • Fritz Albregtsen, "Statistical Texture Measures Computed from Gray Level Coocurrence Matrices ", Image Processing Laboratory Department of Informatics University of Oslo, 2008, November 5.
  • Vapnik, V. N. , "The nature of statistical learning theory". New York, Springer,1995.
  • http://chenlab. ece. cornell. edu/projects/FaceAuthentication
  • Chih-Chung Chang and Chih-Jen Lin "LIBSVM: A library for support vector machines," ACM Transactions on Intelligent Systems and Technology,2011, 2,pp. 1–27.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Gray Level Co-Occurrence Matrix (GLCM) Texture Feature Support Vector Machine

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