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International Workshop on Biometric Authentication

BIOMET 2014: Biometric Authentication pp 165–175Cite as

Facial Expression Classification Using Supervised Descent Method Combined With PCA and SVM

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8897))

Abstract

It has been well known that there is a correlation between facial expression and person’s internal emotional state. In this paper we use an approach to distinguish between neutral and some other expression: based on the displacement of important facial points (coordinates of edges of the mouth, eyes, eyebrows, etc.). Further the feature vectors are formed by concatenating the landmarks data from Supervised Descent Method, applying PCA and use these data as an input to Support Vector Machine (SVM) classifier. The experimental results show improvement of the recognition rate in comparison to some state-of-the-art facial expression recognition techniques.

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References

  1. Tian, Y., Kanade, T., Cohn, J.F.: Facial expression analysis. In: Li, S.Z., Jain, A.K., (Eds.): Handbook of Face Recognition, pp. 247–275. Springer (2005)

    Google Scholar 

  2. Viola, P., Jones, J.M.: Robust real-time object detection. In: Second International Workshop on Statistical and Computational Theories of Vision-Modeling Learning, Computing, and Sampling (2001)

    Google Scholar 

  3. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. In: Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA, pp. 94–101 (2010)

    Google Scholar 

  4. Xuehan-Xiong, De la Torre, F.: Supervised descent method and its application to face alignment, in CVPR (2013)

    Google Scholar 

  5. Chang, C.-C., Lin, C.-J.: LibSVM: a library for support vector machines

    Google Scholar 

  6. Chew, S.W., Lucey, P.J., Lucey, S., Saragih, J., Cohn, J.F., Sridharan, S.: Person-independent facial expression detection using constrained local models. In: Proceedings of FG 2011 Facial Expression Recognition and Analysis Challenge, Santa Barbara, CA (2011)

    Google Scholar 

  7. Taheri, S., Turaga, P., Chellappa, R.: Towards View-Invariant Expression Analysis Using Analytic Shape Manifolds. Int. Conf, Automatic Face and Gesture Recognition (2011)

    Google Scholar 

  8. Li, S., Jain, A.: Handbook of Face Recognition 2nd ed. Springer (2011)

    Google Scholar 

  9. Tian, Y.-L., Kanade, T., Cohn, J.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 1–19 (2001)

    MATH  Google Scholar 

  10. Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying facial actions. IEEETrans. Pattern Anal. Mach. Intell. 21(10), 974–989 (1999)

    Article  Google Scholar 

  11. Susskind, J.M., Littlewort, G., Bartlett, M.S., Movellan, J., Anderson, A.K.: Human and computer recognition of facial expressions of emotion. Neuropsychologia 45, 152–162 (2007)

    Article  Google Scholar 

  12. Littlewort, G., Stewart, M.: Bartlett, I. Fasel, J. Susskind, J. Movellan, Dynamics of facial expression extracted automatically from video. Image and Vision Computing 24, 615–625 (2006)

    Article  Google Scholar 

  13. Sebe, N., Lew, M.S., Sun, Y., Cohen, I., Gevers, T., Huang, T.S.: Authentic facial expression analysis. Image and Vision Computing 25, 1856–1863 (2007)

    Article  Google Scholar 

  14. Shana, C., Gong, S., McOwanb, P.W.: Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing 27, 803–816 (2009)

    Article  Google Scholar 

  15. Tong, Y., Liao, W., Ji, Q.: Automatic Facial Action Unit Recognition by Modeling Their Semantic And Dynamic Relationships, Springer (2009)

    Google Scholar 

  16. Zhi, R., Flierl, M., Ruan, Q., Kleijn, W.: Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition. IEEE, Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(1), 38–52 (2011)

    Google Scholar 

  17. Zafeiriou, S., Petrou, M.: Nonlinear non-negative component analysis algorithms. Image Processing, IEEE Transactions on 19(4), 1050–1066 (2010)

    Article  MathSciNet  Google Scholar 

  18. Jiang, B., Valstary, M.F., Pantic, M.: Facial Action Detection using Block-based Pyramid Appearance Descriptors. In: Proceedings of the ASE/IEEE International Conference on Social Computing (SocialCom 2012). Amsterdam, The Netherlands (September 2012)

    Google Scholar 

  19. Jiang, B., Valstar, M.F., Martinez, B., Pantic, M.: A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modelling. IEEE Transactions of Systems, Man and Cybernetics, Part B. 44(2), 161–174 (2014)

    Google Scholar 

  20. Fellenz, W., Taylor, J., Tsapatsoulis, N., Kollias, S.: Comparing template-based, feature-based and supervised classification of facial expressions from static images. In: Proceedings of Circuits, Systems, Communications and Computers (CSCC 1999), Nugata, Japan, pp. 5331–5336 (1999)

    Google Scholar 

  21. Lanitis, A., Taylor, C., Cootes, T.: Automatic interpretation and coding of face images using 2exible models. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 743–756 (1997)

    Article  Google Scholar 

  22. Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE PAMI 23(6), 681–685 (2001)

    Article  Google Scholar 

  23. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 581–695. Springer, Heidelberg (1998)

    Google Scholar 

  24. Huang, C., Huang, Y.: Facial expression recognition using model-based feature extraction and action parameters classification. J. Visual Commun. Image Representation 8(3), 278–290 (1997)

    Article  Google Scholar 

  25. Wechsler, H.: Reliable Face Recognition Methods: System Design, Implementation and Evaluation, International Series on Biometrics, v. 7, Springer Science & Business Media (2009)

    Google Scholar 

  26. El Ayadi, M., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 44(3), 572–587 (2011)

    Article  MATH  Google Scholar 

  27. Chen, L., Mao, X., Xue, Y., Lung, L.: Cheng. Speech emotion recognition: Features and classification models, Digital Signal Processing 22(6), 1154–1160 (2012)

    Google Scholar 

  28. Ververidis, D., Kotropoulos, C.: Emotional speech recognition: Resources, features, and methods. Speech Communication 48(9), 1162–1181 (2006)

    Article  Google Scholar 

  29. Sandbach, G., Zafeiriou, S., Pantic, M., Yin, L.: Static and dynamic 3D facial expression recognition: A comprehensive survey. Image and Vision Computing 30(10), 683–697 (2012)

    Article  Google Scholar 

  30. Castellano, G., Kessous, L., Caridakis, G.: Multimodal Emotion Recognition from Expressive Faces, Body Gestures and Speech. Proc Doctoral Consortium Second Int’l Conf, Affective Computing and Intelligent Interaction (2007)

    Google Scholar 

  31. Vinay, K.B., Shreyas, B.S.: Face Recognition Using Gabor Wavelets. In: Fortieth Asilomar Conference on Signals, Systems and Computers, ACSSC 2006. pp. 593–597 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Telecommunications, Technical University of Sofia, Sofia, Bulgaria

    Agata Manolova, Nikolay Neshov, Stanislav Panev & Krasimir Tonchev

Authors
  1. Agata Manolova
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  2. Nikolay Neshov
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  3. Stanislav Panev
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  4. Krasimir Tonchev
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Corresponding author

Correspondence to Agata Manolova .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Dimo Dimov

  3. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

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© 2014 Springer International Publishing Switzerland

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Manolova, A., Neshov, N., Panev, S., Tonchev, K. (2014). Facial Expression Classification Using Supervised Descent Method Combined With PCA and SVM. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-13386-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13385-0

  • Online ISBN: 978-3-319-13386-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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