Robust discomfort detection for infants using an unsupervised roll estimation

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

doi:10.1117/12.2512619

15 March 2019 Robust discomfort detection for infants using an unsupervised roll estimation

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

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094930 (2019) https://doi.org/10.1117/12.2512619
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Cheng Li, Arash Pourtaherian, W. E. Tjon A Ten, and Peter H. N. de With "Robust discomfort detection for infants using an unsupervised roll estimation", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094930 (15 March 2019); https://doi.org/10.1117/12.2512619

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