Video-based discomfort detection for infants

Yue Sun, Caifeng Shan (Corresponding author), Tao Tan, Xi Long, Arash Pourtaherian, Svitlana Zinger, Peter H.N. de With

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
185 Downloads (Pure)


Infants are particularly vulnerable to the effects of pain and discomfort, which can lead to abnormal brain development, yielding long-term adverse neurodevelopmental outcomes. In this study, we propose a video-based method for automated detection of their discomfort. The infant face is first detected and normalized. A two-phase classification workflow is then employed, where Phase 1 is subject-independent, and Phase 2 is subject-dependent. Phase 1 derives geometric and appearance features, while Phase 2 incorporates facial landmark-based template matching. An SVM classifier is finally applied to video frames to recognize facial expressions of comfort or discomfort. The method is evaluated using videos from 22 infants. Experimental results show an AUC of 0.87 for the subject-independent phase and 0.97 for the subject-dependent phase, which is promising for clinical use.

Original languageEnglish
Pages (from-to)933-944
Number of pages12
JournalMachine Vision and Applications
Issue number5
Publication statusPublished - 1 Jul 2019


  • Discomfort/stress detection
  • Face detection
  • Facial expression recognition
  • Infant discomfort
  • Discomfort
  • stress detection


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