Robust discomfort detection for infants using an unsupervised roll estimation

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Abstract

Discomfort detection for infants is essential in the healthcare domain, since infants lack the ability to verbalize their pain and discomfort. In this paper, we propose a robust and generic discomfort detection for infants by exploiting a novel and efficient initialization method for facial landmark localization, using an unsupervised rollangle estimation. The roll-angle estimation is achieved by fitting a 1st-order B-spline model to facial features obtained from the scaled-normalized Laplacian of the Gaussian operator. The proposed method can be adopted both for daylight and infrared-light images and supports real-time implementation. Experimental results have shown that the proposed method improves the performance of discomfort detection by 6.0% and 4.2% for the AUC and AP using daylight images, together with 6.9% and 3.8% for infrared-light images, respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Processing
EditorsBennett A. Landman, Elsa D. Angelini
Place of PublicationBellingham
PublisherSPIE
ISBN (Electronic)9781510625457
DOIs
Publication statusPublished - 1 Jan 2019
EventMedical Imaging 2019: Image Processing - San Diego, United States
Duration: 19 Feb 201921 Feb 2019

Publication series

NameProceedings of SPIE
Volume10949

Conference

ConferenceMedical Imaging 2019: Image Processing
CountryUnited States
CitySan Diego
Period19/02/1921/02/19

Keywords

  • B-spline model
  • Discomfort detection
  • Infant
  • Unsupervised roll-angle estimation

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