Skip to main content
SpringerLink
Log in

A Novel Approach for Pain Intensity Detection by KAZE Features

  • Conference paper
  • First Online:
Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 556))

  • 977 Accesses

Abstract

Successful pain evaluation is one of the most difficult problems of clinical practitioners. Continuous pain evaluation of the patient in the intensive care unit (ICU) is burdensome and expensive. An individual, generally a nurse, has to be present at all times to gage the level of pain of a person during as well as after an operation. This research paper is an attempt to automate the pain intensity detection through the scrutiny of the facial features. The intent of this research is to provide a new method of detecting the intensity of pain, i.e., by Kaze features. Kaze features differ from the previous features in the sense that the blurring made by it is locally adaptive. The building of Fisher vector is done through GMM. This paper also reviews the various features used for the purpose of pain detection. The high accuracy of 91.8% instills the confidence in using of Kaze for facial features and makes the realization of an actual state of the art closer than before.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. McCormack HM, David JL, Sheather S (1988) Clinical applications of visual analogue scales: a critical review. Psychol Med 18(4):1007–1019

    Google Scholar 

  2. Jensen MP, Karoly P, Braver S (1986) The measurement of clinical pain intensity: a comparison of six methods. Pain 27(1):117–126

    Article  Google Scholar 

  3. Burckhardt CS, Jones KD (2003) Adult measures of pain: the McGill pain questionnaire (MPQ), rheumatoid arthritis pain scale (RAPS), short‐form mcgill pain questionnaire (SF‐MPQ), verbal descriptive scale (VDS), visual analog scale (VAS), and west haven‐yale multidisciplinary pain inventory (WHYMPI). Arthritis Care Res 49(S5)

    Google Scholar 

  4. Williams ACC, Davies HTO, Chadury Y (2000) Simple pain rating scales hide complex idiosyncratic meanings. Pain 85(3):457–463

    Google Scholar 

  5. Ashraf AB et al (2009) The painful face-pain expression recognition using active appearance models. Image Vision Comput 27(12):1788–1796

    Google Scholar 

  6. Lucey P et al (2011) Painful data: the UNBC-McMaster shoulder pain expression archive database. In: 2011 IEEE international conference on automatic face & gesture recognition and workshops (FG 2011). IEEE

    Google Scholar 

  7. Lucey P et al (2011) Automatically detecting pain in video through facial action units. IEEE Trans Syst Man Cybern Part B (Cybernetics) 41(3):664–674

    Google Scholar 

  8. Lucey P et al (2009) Automatically detecting pain using facial actions. In: 3rd international conference on affective computing and intelligent interaction and workshops, 2009. ACII 2009. IEEE

    Google Scholar 

  9. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154

    Article  Google Scholar 

  10. Dongcheng S, Jieqing J (2010) The method of facial expression recognition based on DWT-PCA/LDA. In: 2010 3rd international congress on image and signal processing (CISP), vol 4. IEEE

    Google Scholar 

  11. Lucey P et al (2009) Automatically detecting action units from faces of pain: comparing shape and appearance features. In: IEEE computer society conference on computer vision and pattern recognition workshops, 2009. CVPR workshops 2009. IEEE

    Google Scholar 

  12. Hammal Z, Cohn JF (2012) Automatic detection of pain intensity. In: Proceedings of the 14th ACM international conference on multimodal interaction. ACM

    Google Scholar 

  13. Rathee N, Ganotra D (2015) A novel approach for pain intensity detection based on facial feature deformations. J Vis Commun Image Represent 33:247–254

    Article  Google Scholar 

  14. Khan RA et al (2013) Pain detection through shape and appearance features. In: 2013 IEEE international conference on multimedia and expo (ICME). IEEE

    Google Scholar 

  15. Kaltwang S, Rudovic O, Pantic M (2012) Continuous pain intensity estimation from facial expressions. Adv Vis Comput 368–377

    Google Scholar 

  16. Feng X, Pietikainen M, Hadid A (2005) Facial expression recognition with local binary patterns and linear programming. Pattern Recogn Image Anal C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 15(2):546

    Google Scholar 

  17. Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816

    Article  Google Scholar 

  18. Sikka K, Dhall A, Bartlett M (2013) Weakly supervised pain localization using multiple instance learning. In: 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG). IEEE

    Google Scholar 

  19. Lucey P et al (2012) Painful monitoring: automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database. Image Vis Comput 30(3):197–205

    Google Scholar 

  20. Rodriguez P, Cucurull G, Gonzàlez J, Gonfaus JM, Nasrollahi K, Moeslund TB, Roca FX (2017) Deep pain: exploiting long short-term memory networks for facial expression classification. IEEE Trans Cybern

    Google Scholar 

  21. Egede J, Valstar M, Martinez B (2017) Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation. In: 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), pp 689–696. IEEE

    Google Scholar 

  22. Alcantarilla PF, Oh SM, Mariottini GL, Bergasa LM, Dellaert F (2010) Learning visibility of landmarks for vision-based localization. In: 2010 IEEE international conference on robotics and automation (ICRA), pp 4881–4888. IEEE

    Google Scholar 

  23. Prkachin KM, Solomon PE (2008) The structure, reliability and validity of pain expression: evidence from patients with shoulder pain. Pain 139(2):267–274

    Article  Google Scholar 

  24. Alcantarilla P, Bartoli A, Davison A (2012) Kaze features. Comput Vis ECCV 2012, 214–227

    Google Scholar 

  25. Weickert J (2001) Efficient image segmentation using partial differential equations and morphology. Pattern Recogn 34(9):1813–1824

    Article  Google Scholar 

  26. Weickert J, Ter Haar Romeny BM, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7(3):398–410

    Google Scholar 

  27. Alcantarilla PF, Solutions T (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Patt Anal Mach Intell 34(7):1281–1298

    Google Scholar 

  28. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IIIT Delhi, New Delhi, India

    Ashutosh Vaish

  2. IIITD, Okhla Industrial Area, New Delhi, India

    Sagar Gupta

Authors
  1. Ashutosh Vaish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sagar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sagar Gupta .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Vijay Nath

  2. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vaish, A., Gupta, S. (2019). A Novel Approach for Pain Intensity Detection by KAZE Features. In: Nath, V., Mandal, J. (eds) Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7091-5_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7090-8

  • Online ISBN: 978-981-13-7091-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation