Abstract
Currently human-computer interaction, especially emotional interaction, still lacks intuition. In health care, it is very important for the medical robot, who assumes the responsibility of taking care of patients, to understand the patient’s feeling, such as happiness and sadness. We propose an approach to facial expression recognition for estimating patients’ emotion. Two expressions (happiness and sadness) are classified in this paper. Our method uses a novel geometric feature parameter, which we call the Emotion Geometry Feature (EGF). The active shape model (ASM), which can be categorized mainly for non-rigid shapes, is used to locate Emotion Geometry Feature (EGF) points. Meanwhile, the Support Vector Machine (SVM) is used to do classification. Our method was tested on a Japanese Female Facial Expression (JAFFE) database. Experimental results, with the average recognition rate of 97.3%, show the efficiency of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aleksic, P.S., Katsaggelos, A.K.: Automatic facial expression recognition using facial animation parameters and multiStream HMMs. IEEE Trans. Inf. Forensics Secur. (2006)
Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)
Gholam Hosseini, H., Krechowec, Z.: Facial Expression Analysis for Estimating Patient’s Emotional States in RPMS. In: Proc. of the 26th Annual International Conference of the IEEE, San Francisco, CA, USA, pp. 1517–1520 (2004)
Ekman, P., Friesen, W.V.: Emotion in the Human Face. Prentice-Hall, Englewood Cliffs (1975)
Fasel, B., Luettin, J.: Automatic Facial Expression analysis: a survey. Pattern Recognition 36, 259–275 (2003)
Pantic, M., Rothkrantz, L.L.M.: Automatic analysis of facial expressions: The state of the art. IEEE Trans. Pattern Ana. and Machine Intelligence 22, 1424–1455 (2000)
Guo, G., Dyer, C.R.: Learning from examples in the small sample case: Face expression recognition. IEEE Trans. Syst., Man, Cybern. B, Cybern. 35, 477–488 (2005)
Ma, L., Khorasani, K.: Facial expression recognition using constructive feedforward neural networks. IEEE Trans. Syst., Man, Cybern. B, Cybern. 34, 1588–1595 (2004)
Essa, I.A., Pentland, A.P.: Facial expression recognition using a dynamic model and motion energy. In: Int. Conf. Computer Vision, Cambrdige, MA (1995)
Bartlett, M.S., Littlewort, G., Braathen, B., Sejnowski, T.J., Movellan, J.R.: An approach to automatic analysis of spontaneous facial expressions. In: 5th IEEE Int. Conf. Automatic Face and Gesture Recognition, Washington, DC (2002)
Ekman, P., Friesen, W.V.: The facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Inc., San Francisco (1978)
Pantic, M., Rothkrantz, L.J.M.: Facial action recognition for facial expression analysis from static face images. IEEE Trans. Sys., Man, Cybern. B: Cybern 34, 1449–1461 (2004)
Zhang, Y., Ji, Q.: Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 699–714 (2005)
Cootes, T.: Introduction to active shape models, technical report (1998)
Kwo, W., Kin, L., Kit, N.: An accurate active shape model for facial feature extraction. In: Proc. Int. Sym. Int. Multimedia, Video and Speech, pp. 109–112 (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification (2008)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: 3rd International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)
Tan, H., Zhang, Y.-J.: Person-Similarity Weighted Feature for Expression Recognition. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 712–721. Springer, Heidelberg (2007)
Tan, H., Zhang, Y.: Person-Independent Expression Recognition Based on Person Similarity Weighted Distance. Jounal of Electronics and Information Technology 29, 455–459 (2007)
Wang, H., Ahuja, N.: Facial Expression Decomposition. In: ICCV, pp. 958–965 (2003)
Tian, Y., Kanade, T., Cohn, J.: Recognizing Action Units for Facial Expression Analysis. IEEE Trans. On PAMI 23, 97–115 (2001)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models- their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Gu, X., Wang, Y., Zhang, L. (2009). Happy-Sad Expression Recognition Using Emotion Geometry Feature and Support Vector Machine. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-03040-6_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
eBook Packages: Computer ScienceComputer Science (R0)