Skip to main content
SpringerLink
Log in

Facial Analysis Method for Pain Detection

  • Conference paper
  • First Online:
Future Access Enablers for Ubiquitous and Intelligent Infrastructures (FABULOUS 2019)

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Histogram of Oriented Gradients. https://www.learnopencv.com/histogram-of-oriented-gradients/. Accessed 01 Feb 2019

  8. Vertan, C., Ciuc, M., Zamfir, M.: Analiza Imaginilor: Îndrumar de laborator, pp. 32–35 (2001)

    Google Scholar 

  9. what-when-how, In Depth Tutorials and Information. http://what-when-how.com/face-recognition/facial-expression-recognition-face-recognition-techniques-part-1/. Accessed 21 Feb 2019

  10. Medium. https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d. Accessed 21 Feb 2019

  11. StackExchange. https://datascience.stackexchange.com/questions/21926/application-of-histogram-of-oriented-gradients-in-colored-image. Accessed 21 Feb 2019

  12. pyimagesearch. https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/. Accessed 26 Feb 2019

  13. Nodulus. https://www.noldus.com/facereader/facial-expression-analysis. Accessed 26 Feb 2019

  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 

Download references

Acknowledgment

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).

Author information

Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, Bucharest, Romania

    Oana Subea

  2. R&D Department BEIA Consult International, Bucharest, Romania

    Oana Subea & George Suciu

Authors
  1. Oana Subea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Suciu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oana Subea .

Editor information

Editors and Affiliations

  1. Department of Communication Networks, Technical University of Sofia, Sofia, Bulgaria

    Vladimir Poulkov

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Subea, O., Suciu, G. (2019). Facial Analysis Method for Pain Detection. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23976-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23975-6

  • Online ISBN: 978-3-030-23976-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation