Cumulative attributes for pain intensity estimation

Joy, Egede and Michel, Valstar (2017) Cumulative attributes for pain intensity estimation. In: 19th ACM International Conference on Multimodal Interaction, 13-17 November 2017, Glasgow, Scotland. (In Press)

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Pain estimation from face video is a hard problem in automatic behaviour understanding. One major obstacle is the di culty of collecting sufficient amounts of data, with balanced amounts of data for all pain intensity levels. To overcome this, we propose to adopt Cumulative Attributes, which assume that attributes for high pain levels with few examples are a superset of all attributes of lower pain levels. Experimental results show a consistent relative performance increase in the order of 20% regardless of features used. Our final system significantly outperforms the state of the art on the UNBC McMaster Shoulder Pain database by using cumulative attributes with Relevance Vector Regression on a combination of features, including appearance, geometric, and deep learned features.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: Proceedings of ICMI ’17, Glasgow, United Kingdom, November 13–17, 2017, ISBN: 9781450355438 ;
Keywords: Pain estimation; Attribute learning; Multi-output regression; Relevance Vector Machines (RVM)
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Valstar, Michel
Date Deposited: 13 Sep 2017 11:05
Last Modified: 04 May 2020 19:17

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