Facial Expression Recognition Based on Perceived Facial Images and Local Feature Matching

  • Hayet Boughrara
  • Liming Chen
  • Chokri Ben Amar
  • Mohamed Chtourou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


Facial expression recognition is to determine the emotional state of the face regardless of its identity. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. This paper presents a biological vision-based facial description, called Perceived Facial Images “PFI” applied to facial expression recognition. For the classification step, Scale Invariant Feature Transform “SIFT” is used to extract a local feature in images. Then, a matching computation is processed between a testing image and all train images for recognizing facial expression. To evaluate, the proposed approach is tested on the GEMEP FERA 2011 database and the Cohn-Kanade Facial Expression database. To compare, the developed algorithm achieves better experimental results than the other approaches in the literature.


Facial Expression Recognition Feature extraction Perceived Facial Images “PFI” SIFT matching 


  1. 1.
    Ekman, P.: Facial expressions of emotion: an old controversy and new findings. Philos. Trans. R. Soc. 335, 63–69 (1992)CrossRefGoogle Scholar
  2. 2.
    Meftah, I.T., Thanh, N.L., Ben Amar, C.: Sharing Emotional Information Using A Three Layer Model. In: Sixth International Conference on Internet and Web Applications and Services, St. Maarten, The Netherlands Antilles, pp. 130–135 (2011)Google Scholar
  3. 3.
    Jemai, O., Zaied, M., Ben Amar, C., Alimi, M.A.: Pyramidal Hybrid Approach: Wavelet Network with OLS Algorithm Based-Image Classification. International Journal of Wavelets, Multiresolution and Information Processing 9(1), 111–130 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Zaied, M., Said, S., Jemai, O., Ben Amar, C.: A novel approach for face recognition based on fast learning algorithm and wavelet network theory. Inter. Journal of Wavelets, Multiresolution and Information Processing 9, 923–945 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Mejdoub, M., Ben Amar, C.: Classification improvement of local feature vectors over the KNN algorithm. International Journal “Multimedia Tools and Applications”, 1–22 (2011) ISSN 1380-7501Google Scholar
  6. 6.
    Meftah, I.T., Thanh, N.L., Ban Amar, C.: Multimodal Recognition of Emotions using a Formal Computational Model. In: International Conference on Signal Image Technology & Internet Systems, Naples, Italy, pp. 541–546 (2012)Google Scholar
  7. 7.
    Meftah, I.T., Thanh, N.L., Ben Amar, C.: Emotion Recognition using KNN Classification for User Modeling and Sharing of Affect States. In: International Conference on Neural Information Processing, Doha, Qatar, pp. 234–242 (2012)Google Scholar
  8. 8.
    Boughrara, H., Chen, L., Ben Amar, C., Chtourou, M.: Face Recognition under varying Facial Expression Based on Perceived Facial Images And Local Feature Matching. In: Inter. Conference on Information Technology and e-Service, Tunisia (2012)Google Scholar
  9. 9.
    Huang, D., Ben Soltana, W., Ardabilian, M., Wang, Y.H., Chen, L.: Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion. In: Workshop on Biometrics. Colorado Springs, CO (2011)Google Scholar
  10. 10.
    Said, S., Ben Amor, B., Ben Amar, C., Daoudi, M.: Fast and Efficient 3D Face Recognition using Wavelet Networks. In: International Conference on Image Processing, Egypt, pp. 4153–4156 (2009)Google Scholar
  11. 11.
    Ben Soltana, W., Ardabilian, M., Chen, L., Ben Amar, C.: A mixture of Gated Experts Optimized using Simulated Annealing for 3D Face Recognition. In: International Conference on Image Processing, Art. N 6116304, Belgium, pp. 3037–3040 (2011)Google Scholar
  12. 12.
    Edelman, S., Intrator, N., Poggio, T.: Complex cells and object recognition (1997) (unpublished manuscript),
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Valstar, M.F., Jiang, B., Mehu, M., Pantic, M., Scherer, K.: The First Facial Expression Recognition and Analysis Challenge. In: Conference on Automatic Face and Gesture Recognition, California (2011)Google Scholar
  15. 15.
    Dhall, A., Asthana, A., Goecke, R., Gedeon, T.: Emotion recognition using phog and lpq features. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 878–883 (2011)Google Scholar
  16. 16.
    Dahmane, M., Meunier, J.: Emotion recognition using dynamic gridbased hog features. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 884–888 (2011)Google Scholar
  17. 17.
    Littlewort, G., Whitehill, J., Wu, T., Butko, N., Ruvolo, P., Movellan, J., Bartlett, M.: The motion in emotion-a cert based approach to the fera emotion challenge. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 897–902 (2011)Google Scholar
  18. 18.
    Meng, H., Romera-Paredes, B., Berthouze, N.: Emotion recognition by two view svm 2k classifier on dynamic facial expression features. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 854–859 (2011)Google Scholar
  19. 19.
    Srivastava, R., Roy, S., Yan, S., Sim, T.: Accumulated motion images for facial expression recognition in videos. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 903–908 (2011)Google Scholar
  20. 20.
    Tariq, U., Lin, K.-H., Li, Z., Zhou, X., Wang, Z., Le, V., Huang, T., Lv, X., Han, T.: Emotion recognition from an ensemble of features. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 872–877 (2011)Google Scholar
  21. 21.
    Yang, S., Bhanu, B.: Facial expression recognition using emotion avatar image. In: Int’l Conf. Automatic Face and Gesture Analysis, pp. 866–871 (2011)Google Scholar
  22. 22.
    Kanade, T., Cohn, J., Tian, Y.L.: Comprehensive database for facial expression analysis. In: Int’l Conference on Automatic Face and Gesture Recognition (2000)Google Scholar
  23. 23.
    Xiao, R., Junzhao, Q., Zhang, D., Shi, P.: Facial expression recognition on multiple manifolds. Pattern Recognition 44, 107–116 (2011)CrossRefzbMATHGoogle Scholar
  24. 24.
    Mehdi, S., Lajevardi, Z., Hussain, M.: Higher order orthogonal moments for invariant facial expression Recognition. Digital Signal Processing 20, 1771–1779 (2010)CrossRefGoogle Scholar
  25. 25.
    Gu, W., Xiang, C., Venkatesh, Y.V., Huang, D., Lin, H.: Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognition 45, 80–91 (2012)CrossRefGoogle Scholar
  26. 26.
    Li, Z.S., Imai, J., Kaneko, M.: Facial expression recognition using facial component based bag of words and PHOG descriptors. Information and Media Technologies 5(3), 1003–1009 (2010)Google Scholar
  27. 27.
    Sanchez, A., Ruiz, J.V., Moreno, A.B., Montemayor, A.S., Hernandez, J., Pantrigo, J.J.: Differential optical flow applied to automatic facial expression recognition. Neurocomputing 74, 1272–1282 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hayet Boughrara
    • 1
  • Liming Chen
    • 2
  • Chokri Ben Amar
    • 3
  • Mohamed Chtourou
    • 1
  1. 1.Control & Energy Management Laboratory, Sfax Engineering SchoolUniversity of SfaxSfaxTunisia
  2. 2.Laboratoire d’InfoRmatique en Image et Systémes d’information, Central School of LyonUniversity of LyonEcullyFrance
  3. 3.Research Group on Intelligent Machines, Sfax Engineering SchoolUniversity of SfaxSfaxTunisia

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