Facial expression recognition has been an active research topic for many years. In this paper a method for automatically recognizing pain intensity in images with facial expressions will be implemented. The method presented will contain a first step in which the face and the important points on the face will be located using the DLIB library. The second step consists of the calculation of HOG-type traits in order to describe the face found. The traits will be used to train a Random Forest (RF) regressor that will estimate the intensity of the pain. Training and testing will be done on the public UNBC-McMaster shoulder Pain Expression Archive database, using Python programming.


Image processing Facial recognition HOG(Histogram of oriented Gradients) Random Forest DLIB 



This work has been supported in part by UEFISCDI Romania and MCI through projects VIRTUOSE, EmoSpaces and PAPUD, funded in part by European Union’s Horizon 2020 research and innovation program under grant agreement No. 777996 (SealedGRID) and No. 787002 (SAFECARE).


  1. 1.
    Tian, Y.-L., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)CrossRefGoogle Scholar
  2. 2.
    Zhang, D., et al. (Eds.) Advanced Data Mining and Applications. In: Proceedings 8th International Conference, ADMA 2012, Nanjing, China, 15–18 December 2012 (2012)Google Scholar
  3. 3.
    Tornincasa, S., et al.: Department of management and production engineering, politecnico di torino. In: 3D Facial Action Units and Expression Recognition using a Crisp Logic (2019)Google Scholar
  4. 4.
    Davidson, Rj., Scherer, K.R., Goldsmith, H.H. (eds.): Introduction: Expression of Emotion, Handbook of Affective Sciences. Oxford University Press, New York (2003)Google Scholar
  5. 5.
    Gholami, B., Haddad, W.M., Tannenbaum, A.R.: Agitation and pain assessment using digital imaging. In: 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, 2–6 September (2009)Google Scholar
  6. 6.
    Nigam, S., Singh, R., Misra, A.K.: Efficient facial expression recognition using histogram of oriented gradients in wavelet domain, Springer Science + Business Media, LLC, part of Springer Nature, pp. 5–10 (2018)Google Scholar
  7. 7.
    Histogram of Oriented Gradients. Accessed 01 Feb 2019
  8. 8.
    Vertan, C., Ciuc, M., Zamfir, M.: Analiza Imaginilor: Îndrumar de laborator, pp. 32–35 (2001)Google Scholar
  9. 9.
    what-when-how, In Depth Tutorials and Information. Accessed 21 Feb 2019
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    Carmen, N., Poenaru, V., Suciu, G.: Heart rate measurement using face detection in video. In: 2018 International Conference on Communications (COMM), pp. 131–134. IEEE (2018)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Faculty of Electronics, Telecommunications and Information TechnologyUniversity Politehnica of BucharestBucharestRomania
  2. 2.R&D Department BEIA Consult InternationalBucharestRomania

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