Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation

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Egede, Joy Onyekachukwu, Valstar, Michel F. and Martinez, Brais (2017) Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation. In: 12th IEEE Conference on Face and Gesture Recognition (FG 2017), 30 May-3 June 2017, Washington, D.C., U.S.A..

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Abstract

Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation risks not being able to benefit from this due to the difficulty in obtaining data sets of considerable size. In this work we propose a combination of hand-crafted and deep-learned features that makes the most of deep learning techniques in small sample settings. Encoding shape, appearance, and dynamics, our method significantly outperforms the current state of the art, attaining a RMSE error of less than 1 point on a 16-level pain scale, whilst simultaneously scoring a 67.3% Pearson correlation coefficient between our predicted pain level time series and the ground truth.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/862392
Additional Information: Published in 2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition (FG 2017). Piscataway, N.J. : IEEE, c2017. Electronic ISBN: 978-1-5090-4023-0. pp. 689-696, doi:10.1109/FG.2017.87 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Pain, Estimation, Feature extraction, Face, Shape, Physiology, Machine learning
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
URLURL Type
http://www.fg2017.org/UNSPECIFIED
Depositing User: Valstar, Michel
Date Deposited: 27 Feb 2017 13:31
Last Modified: 04 May 2020 18:47
URI: https://eprints.nottingham.ac.uk/id/eprint/40801

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