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Facial Displays Description Schemas for Smiling vs. Neutral Emotion Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

Abstract

The possibility of correct automatic recognition of emotion is of high importance in many research domains. In this paper the choice of relevant face regions for smile detection is investigated. Firstly, three facial display division schemas are compared. Afterwards, for the most promising, some region masks are suggested. The presented approach differs in feature vector length. The performance of classification between smiling and neutral facial display proved that applying masks, hence shortening the feature vector, does not decrease the accuracy but even improves the result.

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References

  1. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part I. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  3. Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J.: Measuring facial expressions by computer image analysis. Psychophysiology 36(2), 253–263 (1999)

    Article  Google Scholar 

  4. Cohn, J.F., Zlochower, A.J., Lien, J., Kanade, T.: Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding. Psychophysiology 36, 35–43 (1999)

    Article  Google Scholar 

  5. Cohn, J., Zlochower, A., Lien, J.-J.J., Kanade, T.: Feature-point tracking by optical flow discriminates subtle differences in facial expression. In: Proc. 3rd IEEE Intern. Conf. on Autom. Face and Gesture Recog., pp. 396–401 (1998)

    Google Scholar 

  6. Ekman, P., Friesen, W.: Facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press (1978)

    Google Scholar 

  7. Essa, I.A., Pentland, A.P.: Coding, analysis, interpretation, and recognition of facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 757–763 (1997)

    Article  Google Scholar 

  8. Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as an image preprocessing for face authentication. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 6–14 (2006)

    Google Scholar 

  9. Kawulok, M., Szymanek, J.: Precise multi-level face detector for advanced analysis of facial images. IET Image Processing 6(2), 95–103 (2012)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  11. Liao, S., Fan, W., Chung, A.C.S., Yeung, D.-Y.: Facial expression recognition using advanced local binary patterns, Tsallis entropies and global appearance features. In: IEEE International Conference on Image Processing, pp. 665–668 (2006)

    Google Scholar 

  12. Lien, J.-J.J., Kanade, T., Cohn, J., Li, C.: Detection, tracking, and classification of action units in facial expression. Journal of Robotics and Autonomous System 31, 131–146 (2000)

    Article  Google Scholar 

  13. Mase, K.: An application of optical flow – extraction of facial expression. In: IAPR Workshop on Machine Vision Applications, pp. 195–198 (1990)

    Google Scholar 

  14. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 51–59 (1996)

    Google Scholar 

  15. Ojala, T., Pietikäinen, M., Mäenpää, T.: A generalized Local Binary Pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Singh, S., Murshed, N., Kropatsch, W.G. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 397–406. Springer, Heidelberg (2001)

    Google Scholar 

  16. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  17. Nurzynska, K., Smolka, B.: Optimal Classification Method for Smiling vs. Neutral Facial Display Recognition. Journal of Medical Informatics and Technology 23, 87–94 (2014)

    Google Scholar 

  18. Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1424–1445 (2000)

    Article  Google Scholar 

  19. Pietikäinen, M., Zhao, G., Hadid, A., Ahonen, T.: Computer vision using local binary patterns. Computational imaging and vision, vol. 40, pp. 13–49. Springer (2011)

    Google Scholar 

  20. Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing 27(6), 803–816 (2009)

    Article  Google Scholar 

  21. FERET database, http://www.itl.nist.gov/iad/humanid/feret/feret_master.html (July 04, 2014)

  22. Iranian, Nottingham, Pain, Utrecht database, http://pics.psych.stir.ac.uk (July 04, 2014)

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Correspondence to Karolina Nurzyńska .

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Nurzyńska, K., Smołka, B. (2015). Facial Displays Description Schemas for Smiling vs. Neutral Emotion Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_53

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_53

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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