Robust discomfort detection for infants using an unsupervised roll estimation

Cheng Li, Arash Pourtaherian, W.E. Tjon A. Ten, Peter H.N. de With

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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
ISBN (Electronic)9781510625457
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


ConferenceMedical Imaging 2019: Image Processing
Country/TerritoryUnited States
CitySan Diego


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


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