Abstract
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBC-McMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.
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Lundtoft, D.H., Nasrollahi, K., Moeslund, T.B., Escalera, S. (2016). Spatiotemporal Facial Super-Pixels for Pain Detection. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_4
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DOI: https://doi.org/10.1007/978-3-319-41778-3_4
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