Abstract
Neonatal pain assessment is essential for infants concerning their health issues. There have been several studies to assess the pain of infants using image processing in the field of computer vision. In this paper, we propose a different approach to detect pain in infants that outperforms previous research in this field. We merged a face area-based feature collection method with a local binary pattern (LBP). Moreover, three different machine learning algorithms have been executed to find the best parameter to get a decent accuracy on the iCOPE dataset. The proposed method uses the SVM classifier to achieve 86% of testing accuracy compared to other methods.
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Ritu, J.T., Shakil, M.S.H., Hasan, M.N.I., Al Mamun, S., Kaiser, M.S., Mahmud, M. (2022). Facial Detection for Neonatal Infant Pain Using Facial Geometry Features and LBP. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_42
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