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International Conference on Computer Vision Systems

ICVS 2015: Computer Vision Systems pp 128–136Cite as

Learning Appearance Features for Pain Detection Using the UNBC-McMaster Shoulder Pain Expression Archive Database

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9163))

Abstract

We propose a supervised approach to solve the task of automatic pain detection from facial expressions. A pain detection algorithm should be both robust to face pose and the identity of the face (identity bias). In order to achieve invariance to face pose, we use an Active Appearance Model (AAM) to warp all face images into frontal pose. The main contribution of our paper is a discriminative feature extractor that addresses identity bias by learning appearance features that separate pain-related factors from other factors, such as those related to the identity of the face. The system achieves state-of-the-art performance on the UNBC-McMaster Shoulder Pain Expression Archive Database.

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Author information

Authors and Affiliations

  1. Department of Engineering, Aarhus University, Finlandsgade 22, Aarhus, Denmark

    Henrik Pedersen

Authors
  1. Henrik Pedersen
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Correspondence to Henrik Pedersen .

Editor information

Editors and Affiliations

  1. Aalborg University, Copenhagen, Denmark

    Lazaros Nalpantidis

  2. Aalborg University, Copenhagen, Denmark

    Volker Krüger

  3. Royal Institute of Technology - KTH, Stockholm, Sweden

    Jan-Olof Eklundh

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Gasteratos

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© 2015 Springer International Publishing Switzerland

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Pedersen, H. (2015). Learning Appearance Features for Pain Detection Using the UNBC-McMaster Shoulder Pain Expression Archive Database. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-20904-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20903-6

  • Online ISBN: 978-3-319-20904-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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