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Cumulative attributes for pain intensity estimation

Joy, Egede; Michel, Valstar

Authors

Egede Joy



Abstract

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.

Citation

Joy, E., & Michel, V. (2017). Cumulative attributes for pain intensity estimation. https://doi.org/10.1145/3136755.3136789

Conference Name 19th ACM International Conference on Multimodal Interaction
Start Date Nov 13, 2017
End Date Nov 17, 2017
Acceptance Date Aug 11, 2017
Publication Date Nov 13, 2017
Deposit Date Sep 13, 2017
Publicly Available Date Nov 13, 2017
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Pages 146-153
ISBN 978-1-4503-5543-8
DOI https://doi.org/10.1145/3136755.3136789
Keywords Pain estimation; Attribute learning; Multi-output regression; Relevance Vector Machines (RVM)
Public URL http://eprints.nottingham.ac.uk/id/eprint/45830
Publisher URL https://dl.acm.org/citation.cfm?id=3136789
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information Published in: Proceedings of ICMI ’17, Glasgow, United Kingdom, November 13–17, 2017, ISBN: 9781450355438 ;
https://doi.org/10.1145/3136755.3136789

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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