Behavioral State Detection of Newborns Based on Facial Expression Analysis
Prematurely born infants are observed at a Neonatal Intensive Care Unit (NICU) for medical treatment. Whereas vital body functions are continuously monitored, their incubator is covered by a blanket for medical reasons. This prevents visual observation of the newborns during most time of the day, while it is known that the facial expression can give valuable information about the presence of discomfort.
This prompted the authors to develop a prototype of an automated video survey system for the detection of discomfort in newborn babies by analysis of their facial expression. Since only a reliable and situation-independent system is useful, we focus at robustness against non-ideal viewpoints and lighting conditions. Our proposed algorithm automatically segments the face from the background and localizes the eye, eyebrow and mouth regions. Based upon measurements in these regions, a hierarchical classifier is employed to discriminate between the behavioral states sleep, awake and cry.
We have evaluated the described prototype system on recordings of three healthy newborns, and we show that our algorithm operates with approximately 95% accuracy. Small changes in viewpoint and lighting conditions are allowed, but when there is a major reduction in light, or when the viewpoint is far from frontal, the algorithm fails.
KeywordsFacial Expression Mouth Region Viewpoint Change Healthy Newborn Heel Lance
Unable to display preview. Download preview PDF.
- 3.Chen, K., Chang, S., Hsiao, T., Chen, Y., Lin, C.: A neonatal facial image scoring system (NFISS) for pain response studies. BME ABC, 79–85 (2005)Google Scholar
- 5.Brahnam, S., Chuang, C., Sexton, R.S., Shih, F.Y.: Machine assessment of neonatal facial expressions of acute pain. Special Issue on Decision Support in Medicine in Decision Support Systems 43, 1247–1254 (2007)Google Scholar
- 6.Peng, K., Chen, L.: A Robust Algorithm for Eye Detection on Gray Intensity Face without Spectacles. Journal of Computer Science and Technology, 127–132 (2005)Google Scholar
- 7.Vezhnevets, V., Degtiareva, A.: Robust and Accurate Eye Contour Extraction. In: Proc. Graphicon, pp. 81–84 (2003)Google Scholar
- 8.Asteriadis, S., Nikolaidis, N., Hajdu, A., Pitas, I.: A novel eye-detection algorithm utilizing edge-related geometrical information. In: EUSIPCO 2006 (2006)Google Scholar
- 10.Michel, P., Kaliouby, R.: Real time facial expression recognition in video using support vector machines. In: ICMI (2003)Google Scholar
- 14.Gomez, E., Travieso, C.M., Briceno, J.C., Ferrer, M.A.: Biometric identification system by lip shape, Security Technology (2002)Google Scholar
- 16.Gocke, R., Millar, J.B., Zelensky, A., Robert-Ribes, J.: Automatic extraction of lip feature points. In: Proc. of ACRA 2000, pp. 31–36 (2000)Google Scholar