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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

A Novel Approach for Pain Intensity Detection by KAZE Features

verfasst von : Ashutosh Vaish, Sagar Gupta

Erschienen in: Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems

Verlag: Springer Singapore

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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.

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Literatur
1.
McCormack HM, David JL, Sheather S (1988) Clinical applications of visual analogue scales: a critical review. Psychol Med 18(4):1007–1019
2.
Jensen MP, Karoly P, Braver S (1986) The measurement of clinical pain intensity: a comparison of six methods. Pain 27(1):117–126CrossRef
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)
4.
Williams ACC, Davies HTO, Chadury Y (2000) Simple pain rating scales hide complex idiosyncratic meanings. Pain 85(3):457–463
5.
Ashraf AB et al (2009) The painful face-pain expression recognition using active appearance models. Image Vision Comput 27(12):1788–1796
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
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
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
9.
Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154CrossRef
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
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
12.
Hammal Z, Cohn JF (2012) Automatic detection of pain intensity. In: Proceedings of the 14th ACM international conference on multimodal interaction. ACM
13.
Rathee N, Ganotra D (2015) A novel approach for pain intensity detection based on facial feature deformations. J Vis Commun Image Represent 33:247–254CrossRef
14.
Khan RA et al (2013) Pain detection through shape and appearance features. In: 2013 IEEE international conference on multimedia and expo (ICME). IEEE
15.
Kaltwang S, Rudovic O, Pantic M (2012) Continuous pain intensity estimation from facial expressions. Adv Vis Comput 368–377
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
17.
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816CrossRef
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
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
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
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
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
23.
Prkachin KM, Solomon PE (2008) The structure, reliability and validity of pain expression: evidence from patients with shoulder pain. Pain 139(2):267–274CrossRef
24.
Alcantarilla P, Bartoli A, Davison A (2012) Kaze features. Comput Vis ECCV 2012, 214–227
25.
Weickert J (2001) Efficient image segmentation using partial differential equations and morphology. Pattern Recogn 34(9):1813–1824CrossRef
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
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
28.
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Metadaten
Titel
A Novel Approach for Pain Intensity Detection by KAZE Features
verfasst von
Ashutosh Vaish
Sagar Gupta
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems

Print ISBN: 978-981-13-7090-8

Electronic ISBN: 978-981-13-7091-5

Copyright-Jahr: 2019

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

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