Abstract
This paper proposes asystem forthe facial expression recognition. Firstly, we perform noise reduction by a median filter of facial expression image. Then, a cross-correlation of optical flow and mathematical models from the facial points are used. To define these facial points of interest in the first frame of an input face sequence image, which utilize manually marker. The facial points were automatically tracked by a cross-correlation, which is based on optical flow,and then extracted the feature vectors. The mathematical model extracts features from the feature vectors. An ELMAN neural network was applied to classify expressions. The performances of the proposed facial expressions recognition were computed by Cohn–Kanade facial expressions database. This proposed approach achieved a high recognition rate.
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References
Tukey, J.W.: Nonlinear (nonsuperposable) Methods for Smoothing Data. In: Proc. Congr. Rec. EASCOM, vol. 74, p. 673 (1974)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Ekman, P., Friesen, W.V., O’Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., Krause, R., Lecompte, W.A., Pitcairn, T., Ricci-Bitt, P.E., et al.: Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion. J. Pers. Soc. Psychol. 53, 712–717 (1987)
Ekman, P., Friesen, W.V.: Facial Action Coding System. Consulting Psychologist Press, Palo Alto (1978)
Ekman, P., Friesen, W.: Pictures of Facial Affect. Consulting Psychol. (1976)
Gizatdinova, Y., Surakka, V.: Feature-Based Detection of Facial Landmarks from Neutral and Expressive Facial Images. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 135–139 (2006)
Cohn, J.F., Zlochower, A.J., Lien, J.J., Kanade, T.: Feature-Point Tracking by Optical Flow Discriminates Subtle Difference in Facial Expression. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 396–401 (1998)
Mase, K.: Recognition of Facial Expression from Optical Flow. IEICE Transactions, E 74, 3474–3483 (1991)
Seyedarabi, H., Aghagolzadeh, A., Khanmohammadi, S.: Recognition of Six Basic Facial Expressions by Feature-Points Tracking using RBF Neural Networks and Fuzzy Inference System. In: The IEEE International Conference on Multimedia & Expo (ICME 2004), Taipei, Taiwan (June 2004)
Seyedarabi, H., Aghagolzadeh, A., Khanmohammadi, S.: Facial Expressions Recognition from Static Images Using Neural Networks and Fuzzy Logic. In: The 2nd Iranian Conference on Machine Vision and Image Processing (MVIP 2003), Tehran, vol. 1, pp. 7–12 (2003)
Black, M.J., Yacoob, Y.: Recognizing Facial Expressions under Rigid and Non-Rigid Facial Motions. In: International Workshop on Automatic Face and Gesture Recognition, Zurich, pp. 12–17 (1995)
Rosenblum, M., Yacoob, Y., Davis, L.S.: Human Emotion Recognition from Motion Using a Radial Basis Function Network Architecture. In: Proceedings of the Workshop on Motion of Non-rigid and Articulated Objects, Austin, TX (1994)
Medioni, G., Kang, S.B.: Emerging Topics in Computer Vision. Prentice-Hall, Englewood Cliffs (2004)
Christodoulou, C., Georg, M.: Applications of Neural Networks in Electromagnetics. Artech House (2001)
Medsker, L., Jain, L.C.: Recurrent Neural Networks Design and Applications. CRC, Boca Raton (1999)
Samarasinghe, S.: Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition. AUERBACH (2006)
Kumar, V.V., Krishnamurthy, A.K., Ahalt, S.C.: Phonetic-to-Acoustic and Acoustic-to- Phonetic Mapping Using Recurrent Neural Networks. Applications of Artificial Neural Networks (SPIE) 1469, 484–494 (1991)
Topouzelis, K., Karathanassi, V., Pavlakis, P., Rokos, D.: Dark Formation Detection Using Recurrent Neural Networks and SAR Data. In: Proc. of SPIE, vol. 6365, pp. 111–117 (2006)
Lee, S.W., Lee, E.J.: Integrated Segmentation and Recognition of Connected Handwritten Characters with Recurrent Neural Network. In: Proc. SPIE, vol. 2660, pp. 251–261 (1996)
Buenaposada, J.M., Munoz, E., Baumela, L.: Recognising Facial Expressions in Video Sequences. Pattern Anal. Applic. 11, 101–116 (2008)
Lu, H., Wang, Z., Liu, X.: Facial Expression Recognition Using NKFDA Method With Gabor Features. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China (2006)
Datcu, D., Rothkrantz, L.J.M.: Facial Expression Recognition with Relevance Vector Machines. In: IEEE International Conference on Multimedia and Expo, pp. 193–196 (2005)
Tai, S.C., Huang, H.F., Chung, K.C.: Automatic Facial Expression Discrimination System. Far East Journal of Electronics and Communication 1, 23–31 (2007)
Lu, H.C., Huang, Y.J., Chen, Y.W., Yang, D.I.: Real-time Facial Fxpression Recognition Based on Pixel-pattern-based Texture Feature. Electronics Letters 43(17) (August 16, 2007)
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Tai, S., Huang, H. (2009). Facial Expression Recognition in Video Sequences. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_113
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DOI: https://doi.org/10.1007/978-3-642-01513-7_113
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