A Computational Framework for Measuring the Facial Emotional Expressions

  • Hassan UgailEmail author
  • Ahmad Ali Asad Aldahoud
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


The purpose of this chapter is to discuss and present a computational framework for detecting and analysing facial expressions efficiently. The approach here is to identify the face and estimate regions of facial features of interest using the optical flow algorithm. Once the regions and their dynamics are computed a rule based system can be utilised for classification. Using this framework, we show how it is possible to accurately identify and classify facial expressions to match with FACS coding and to infer the underlying basic emotions in real time.


Location based search Facial feature detection Regions of interest Viola Jones algorithm 


  1. 1.
    Al-dahoud, A., Ugail, H.: A method for location based search for enhancing facial feature detection. In: Angelov P., Gegov A., Jayne C., Shen Q. (eds.) Advances in Intelligent Systems and Computing, pp. 421–432. Springer, Cham (2016)Google Scholar
  2. 2.
    About OpenCV, 20/11 (2015).
  3. 3.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)Google Scholar
  4. 4.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Comparing active shape models with active appearance models. BMVC 1999, 173–182 (1999)Google Scholar
  5. 5.
    Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) Image Analysis, SCIA 2003. Lecture Notes in Computer Science, vol. 2749, pp 363–370. Springer, Heidelberg (2003)Google Scholar
  6. 6.
    Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. In: Computer Vision, ECCV 2008, pp. 504–513. Springer, Heidelberg (2008)Google Scholar
  7. 7.
    Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of IEEE computer society conference on Computer Vision and Pattern Recognition, pp. 130–136. IEEE (1997)Google Scholar
  8. 8.
    Pai, Y-T., Ruan, S-J., Shie, M-C., Liu Y-C.: A simple and accurate color face detection algorithm in complex background. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 1545–1548. IEEE (2006)Google Scholar
  9. 9.
    Ping, S.T.Y., Weng, C.H., Lau, B.: Face detection through template matching and color segmentation. Nevim. Nevim 89 (2003)Google Scholar
  10. 10.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998)Google Scholar
  11. 11.
    Singh, S.K., Chauhan, D., Vatsa, M., Singh, R.: A robust skin color based face detection algorithm. Tamkang J. Sci. Eng. 6(4), 227–234 (2003)Google Scholar
  12. 12.
    Valstar, M.F., Mehu, M., Jiang, B., Pantic, M., Scherer, K.: Meta-analysis of the first facial expression recognition challenge. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(4), 966–979 (2012)Google Scholar
  13. 13.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)Google Scholar
  14. 14.
    Yap, M.H., Ugail, H., Zwiggelaar, R., Rajoub, B., Doherty, V., Appleyard, S., Huddy. G.: A short review of methods for face detection and multifractal analysis. In: Cyberworlds 2009, Bradford, UK (2009)Google Scholar
  15. 15.
    Yap, M.H., Ugail, H., Zwiggelaar, R., Rajoub, B.: Facial image processing for facial analysis. In: IEEE International Carnahan Conference on Security Technology (ICCST 2010), California, USA, San Jose (2010)Google Scholar
  16. 16.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Intensity score for facial actions detection in near-frontal-view face sequences. Comput. Commun. Eng. 6, 819–824 (2013)Google Scholar
  17. 17.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Facial analysis for real-time application: a review in visual cues detection techniques. J. Commun. Comput. 9, 1231–1241 (2013)Google Scholar
  18. 18.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Facial behavioural analysis: a case study in deception detection. Br. J. Appl. Sci. Technol. 4(10), 1485–1496 (2014)Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and InformaticsUniversity of BradfordBradfordUK
  2. 2.Faculty of Engineering and MathematicsUniversity of BradfordBradfordUK

Personalised recommendations