Advertisement

Improved Performance in Facial Expression Recognition Using 32 Geometric Features

  • Giuseppe PalestraEmail author
  • Adriana Pettinicchio
  • Marco Del Coco
  • Pierluigi Carcagnì
  • Marco Leo
  • Cosimo Distante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

Automatic facial expression recognition is one of the most interesting problem as it impacts on important applications in human-computer interaction area. Many applications in this field require real-time performance but not all the approach are suitable to satisfy this requirement. Geometrical features are usually the most light in terms of computational load but sometimes they exploits a huge number of features and do not cover all the possible geometrical aspect. In order to face up this problem, we propose an automatic pipeline for facial expression recognition that exploits a new set of 32 geometric facial features from a single face side covering a wide set of geometrical peculiarities. As a results, the proposed approach showed a facial expression recognition accuracy of 95,46% with a six-class expression set and an accuracy of 94,24% with a seven-class expression set.

Keywords

Facial expression recognition Human-computer interaction Geometric features Random forest 

References

  1. 1.
    Pantic, M., Rothkrantz, L.: Automatic analysis of facial expressions: The state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1424–1445 (2000)CrossRefGoogle Scholar
  2. 2.
    Ekman, P., Friesen, W.: In Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vision. 57, 137–154 (2004)CrossRefGoogle Scholar
  4. 4.
    Loconsole, C., Runa Miranda, C., Augusto, G., Frisoli, A., Orvalho, V.: Real-time emotion recognition: a novel method for geometrical facial features extraction. In: Proc. of 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, January 5–8, Lisbon, Portugal (2014)Google Scholar
  5. 5.
    Bevilacqua, V., D’Ambruoso, D., Mandolino, G., Suma, M.: A new tool to support diagnosis of neurological disorders by means of facial expressions. In: Proceedings of IEEE 2011 International Workshop on Medical Measurements and Applications Proceedings, May 30–31, Bari, Italy pp. 544–549 (2011)Google Scholar
  6. 6.
    Asthana, A., Saragih, J., Wagner, M., Goecke, R.: Evaluating AAM Fitting Methods for Facial Expression Recognition. In: Proc. of the Inter. Conf. on Affective Computing and Intelligent Interaction, September 10–12, Amsterdam, pp. 1–8 (2009)Google Scholar
  7. 7.
    Milborrow, S., Nicolls, F.: Active Shape Models with SIFT Descriptors and MARS. In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications, January 5–8, Lisbon, Portugal (2014)Google Scholar
  8. 8.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z.: The Extended Cohn-Kanade Dataset (CK+): A Complete Dataset for Action Unit and Emotion-Specified Expression. In: Proc. of the 3rd IEEE Workshop on CVPR for Human Communication Behavior Analysis, June 13–18, San Francisco, CA, USA, pp. 94–101 (2010)Google Scholar
  9. 9.
    Ghimire, D., Lee, J.: Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines. Sensors 13, 7714–7734 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhao, X., Zhang, S.: Facial expression recognition based on local binary patterns and kernel discriminant isomap. Sensors 11, 9573–9588 (2011)CrossRefGoogle Scholar
  11. 11.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)CrossRefGoogle Scholar
  12. 12.
    Jabid, T., Kabir, M.H., Chae, O.: Robust facial expression recognition based on local directional pattern. ETRI J 32, 784–794 (2010)CrossRefGoogle Scholar
  13. 13.
    Shan, C., Gong, S., McOwan, P.: Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis. Comput. 27, 803–816 (2009)CrossRefGoogle Scholar
  14. 14.
    Rao, K.S., Saroj, V.K., Maity, S., Koolagudi, S.G.: Recognition of emotions from video using neural network models. Expert Systems with Applications 38, 13181–13185 (2011)CrossRefGoogle Scholar
  15. 15.
    Khanum, A., Mufti, M., Javed, M.Y., Shafiq, M.Z.: Fuzzy case-based reasoning for facial expression recognition. Fuzzy Sets and Systems 160, 231–250 (2009)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Youssif, A.A., Asker, W.A.: Automatic facial expression recognition system based on geometric and appearance features. Computer and Information Science 4, 115–124 (2011)Google Scholar
  17. 17.
    Martiriggiano, T., Leo, M., D’Orazio, T., Distante, A.: Face Recognition by Kernel Independent Component Analysis. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 55–58. Springer, Heidelberg (2005) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giuseppe Palestra
    • 1
    Email author
  • Adriana Pettinicchio
    • 2
  • Marco Del Coco
    • 2
  • Pierluigi Carcagnì
    • 2
  • Marco Leo
    • 2
  • Cosimo Distante
    • 2
  1. 1.Department of Computer ScienceUniversity of BariBariItaly
  2. 2.National Institute of OpticsNational Research CouncilArnesanoItaly

Personalised recommendations