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	Author	Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera
	Title	Spatiotemporal Facial Super-Pixels for Pain Detection			Type	Conference Article
	Year	2016	Publication	9th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Facial images; Super-pixels; Spatiotemporal filters; Pain detection
	Abstract	Best student paper award. 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 UNBCMcMaster 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.
	Address	Palma de Mallorca; Spain; July 2016
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	AMDO
	Notes	HUPBA;MILAB			Approved	no
	Call Number	Admin @ si @ LNM2016			Serial	2847
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