Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP

Jarin Tasnim Ritu; Md Shahadat Hossen Shakil; Md Nahian Imtiaz Hasan; Shamim Al Mamun; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-981-16-7597-3_42

Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP

Jarin Tasnim Ritu^*
, Md Shahadat Hossen Shakil
, Md Nahian Imtiaz Hasan
, Shamim Al Mamun
, M. Shamim Kaiser
, Mufti Mahmud

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Neonatal pain assessment is essential for infants concerning their health issues. There have been several studies to assess the pain of infants using image processing in the field of computer vision. In this paper, we propose a different approach to detect pain in infants that outperforms previous research in this field. We merged a face area-based feature collection method with a local binary pattern (LBP). Moreover, three different machine learning algorithms have been executed to find the best parameter to get a decent accuracy on the iCOPE dataset. The proposed method uses the SVM classifier to achieve 86% of testing accuracy compared to other methods.

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021
Editors	M. Shamim Kaiser, Kanad Ray, Anirban Bandyopadhyay, Kavikumar Jacob, Kek Sie Long
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	509-518
Number of pages	10
ISBN (Print)	9789811675966
DOIs	https://doi.org/10.1007/978-981-16-7597-3_42
State	Published - 2022
Externally published	Yes
Event	3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021 - Parit Raja, Malaysia Duration: 21 Oct 2021 → 22 Oct 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	348
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021
Country/Territory	Malaysia
City	Parit Raja
Period	21/10/21 → 22/10/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Facialgeometry
LBP
Neonates
Pain detection
iCOPE

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-16-7597-3_42

Cite this

Ritu, J. T., Shakil, M. S. H., Hasan, M. N. I., Al Mamun, S., Kaiser, M. S., & Mahmud, M. (2022). Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. In M. S. Kaiser, K. Ray, A. Bandyopadhyay, K. Jacob, & K. S. Long (Eds.), Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021 (pp. 509-518). (Lecture Notes in Networks and Systems; Vol. 348). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7597-3_42

Ritu, Jarin Tasnim ; Shakil, Md Shahadat Hossen ; Hasan, Md Nahian Imtiaz et al. / Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. editor / M. Shamim Kaiser ; Kanad Ray ; Anirban Bandyopadhyay ; Kavikumar Jacob ; Kek Sie Long. Springer Science and Business Media Deutschland GmbH, 2022. pp. 509-518 (Lecture Notes in Networks and Systems).

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title = "Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP",

abstract = "Neonatal pain assessment is essential for infants concerning their health issues. There have been several studies to assess the pain of infants using image processing in the field of computer vision. In this paper, we propose a different approach to detect pain in infants that outperforms previous research in this field. We merged a face area-based feature collection method with a local binary pattern (LBP). Moreover, three different machine learning algorithms have been executed to find the best parameter to get a decent accuracy on the iCOPE dataset. The proposed method uses the SVM classifier to achieve 86\% of testing accuracy compared to other methods.",

keywords = "Facialgeometry, LBP, Neonates, Pain detection, iCOPE",

author = "Ritu, \{Jarin Tasnim\} and Shakil, \{Md Shahadat Hossen\} and Hasan, \{Md Nahian Imtiaz\} and \{Al Mamun\}, Shamim and Kaiser, \{M. Shamim\} and Mufti Mahmud",

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Ritu, JT, Shakil, MSH, Hasan, MNI, Al Mamun, S, Kaiser, MS & Mahmud, M 2022, Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. in MS Kaiser, K Ray, A Bandyopadhyay, K Jacob & KS Long (eds), Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. Lecture Notes in Networks and Systems, vol. 348, Springer Science and Business Media Deutschland GmbH, pp. 509-518, 3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021, Parit Raja, Malaysia, 21/10/21. https://doi.org/10.1007/978-981-16-7597-3_42

Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. / Ritu, Jarin Tasnim; Shakil, Md Shahadat Hossen; Hasan, Md Nahian Imtiaz et al.
Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. ed. / M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long. Springer Science and Business Media Deutschland GmbH, 2022. p. 509-518 (Lecture Notes in Networks and Systems; Vol. 348).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Neonatal pain assessment is essential for infants concerning their health issues. There have been several studies to assess the pain of infants using image processing in the field of computer vision. In this paper, we propose a different approach to detect pain in infants that outperforms previous research in this field. We merged a face area-based feature collection method with a local binary pattern (LBP). Moreover, three different machine learning algorithms have been executed to find the best parameter to get a decent accuracy on the iCOPE dataset. The proposed method uses the SVM classifier to achieve 86% of testing accuracy compared to other methods.

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Ritu JT, Shakil MSH, Hasan MNI, Al Mamun S, Kaiser MS, Mahmud M. Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. In Kaiser MS, Ray K, Bandyopadhyay A, Jacob K, Long KS, editors, Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 509-518. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-16-7597-3_42