Recognize Emotions from Facial Expressions Using a SVM and Neural Network Schema

  • Isidoros PerikosEmail author
  • Epaminondas Ziakopoulos
  • Ioannis Hatzilygeroudis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


Emotions are important and meaningful aspects of human behaviour. Analyzing facial expressions and recognizing their emotional state is a challenging task with wide ranging applications. In this paper, we present an emotion recognition system, which recognizes basic emotional states in facial expressions. Initially, it detects human faces in images using the Viola-Jones algorithm. Then, it locates and measures characteristics of specific regions of the facial expression such as eyes, eyebrows and mouth, and extracts proper geometrical characteristics form each region. These extracted features represent the facial expression and based on them a classification schema, which consists of a Support Vector Machine (SVM) and a Multilayer Perceptron Neural Network (MLPNN), recognizes each expression’s emotional content. The classification schema initially recognizes whether the expression is emotional and then recognizes the specific emotions conveyed. The evaluation conducted on JAFFE and Kohn Kanade databases, revealed very encouraging results.


Emotion recognition Facial gestures Human-computer interaction Support vector machines Multilayer Perceptron Neural Network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akputu, K.O., Seng, K.P., Lee, Y.L.: Facial emotion recognition for intelligent tutoring environment. In: 2nd International Conference on Machine Learning and Computer Science (IMLCS 2013), pp. 9–13 (2013)Google Scholar
  2. 2.
    Aung, D.M., Aye, N.A.: Facial expression classification using histogram based method. In: International Conference on Signal Processing Systems (2012)Google Scholar
  3. 3.
    Bettadapura, V.: Face expression recognition and analysis: the state of the art. arXiv preprint arXiv:1203.6722 (2012)Google Scholar
  4. 4.
    Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45–60 (1999)Google Scholar
  5. 5.
    Filko, D., Goran, M.: Emotion recognition system by a neural network based facial expression analysis. Autom.- J. Control Meas. Electron Comput. Commun. 54, 263–272 (2013)Google Scholar
  6. 6.
    Kanade, T., Cohn, J.F., Tian, Y: Comprehensive database for facial expression analysis. In: Proceedings of the Automatic Face and Gesture Recognition (2000)Google Scholar
  7. 7.
    Koutlas, A., Fotiadis, D.I.: An automatic region based methodology for facial expression recognition. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 662–666 (2008)Google Scholar
  8. 8.
    Lozano-Monasor, E., López, M.T., Fernández-Caballero, A., Vigo-Bustos, F.: Facial expression recognition from webcam based on active shape models and support vector machines. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 147–154. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Lyons, M.J., Akamatsu,S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  10. 10.
    Mehrabian, A.: Communication without words. Psychology Today 2(4), 53–56 (1968)Google Scholar
  11. 11.
    Michel, P., El Kaliouby, R.: Facial expression recognition using support vector machines. In: The 10th International Conference on Human-Computer Interaction, Greece (2005)Google Scholar
  12. 12.
    Perikos, I., Ziakopoulos, E., Hatzilygeroudis, I.: Recognizing emotions from facial expressions using neural network. In: Iliadis, L. (ed.) AIAI 2014. IFIP AICT, vol. 436, pp. 236–245. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Přinosil, J., Smékal, Z., Esposito, A.: Combining features for recognizing emotional facial expressions in static images. In: Esposito, A., Bourbakis, N.G., Avouris, N., Hatzilygeroudis, I. (eds.) HH and HM Interaction. LNCS (LNAI), vol. 5042, pp. 56–69. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Shen, L., Wang, M., Shen, R.: Affective e - learning: Using “emotional” data to improve learning in pervasive learning environment. Educational Technology & Society 12(2), 176–189 (2009)Google Scholar
  15. 15.
    Thai, L.H., Nguyen, N.D.T., Hai, T.S.: A facial expression classification system integrating canny, principal component analysis and artificial neural network. arXiv preprint arXiv:1111.4052 (2011)Google Scholar
  16. 16.
    Valstar, M.F., Mehu, M., Jiang, B., Pantic, M., Scherer, K.: Meta-analysis of the first facial expression recognition challenge. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(4), 966–979 (2012)CrossRefGoogle Scholar
  17. 17.
    Verma, A., Sharma, L.K.: A Comprehensive Survey on Human Facial Expression Detection. International Journal of Image Processing (IJIP) 7(2), 171 (2013)Google Scholar
  18. 18.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  19. 19.
    Visutsak, P.: Emotion Classification through Lower Facial Expressions using Adaptive Support Vector Machines. JMMT: Journal of Man, Machine and Technology 2(1), 12–20 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Isidoros Perikos
    • 1
    Email author
  • Epaminondas Ziakopoulos
    • 1
  • Ioannis Hatzilygeroudis
    • 1
  1. 1.School of Engineering, Department of Computer Engineering & InformaticsUniversity of PatrasPatrasGreece

Personalised recommendations