Video-based discomfort detection for infants

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

研究成果: Article同行評審

13 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)933-944
頁數12
期刊Machine Vision and Applications
30
發行號5
DOIs
出版狀態Published - 1 7月 2019
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