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Authors: Niloufar Zebarjadi and Iman Alikhani

Affiliation: University of Oulu, Finland

Keyword(s): Regression, LBP-TOP, 3D-SIFT, LBP, DSIFT, Feature Extraction, Facial Expression Analysis.

Related Ontology Subjects/Areas/Topics: Affective Computing ; Biomedical Engineering ; Health Information Systems ; Pattern Recognition and Machine Learning

Abstract: Self-report is the most conventional means of pain intensity assessment in clinical environments. But, it is not an accurate metric or not even possible to measure in many circumstances, e.g. intensive care units. Continuous and automatic pain level evaluation is an advantageous solution to overcome this issue. In this paper, we aim to map facial expressions to pain intensity levels. We extract well-known static (local binary pattern(LBP) and dense scale-invariant feature transform (DSIFT)) and dynamic (local binary patterns on three orthogonal planes (LBP-TOP) and three dimensional scale-invariant feature transform (3D-SIFT)) facial feature descriptors and employ the linear regression method to label a number between zero (no pain) to five (strong pain) to each testing sequence. We have evaluated our methods on the publicly available UNBC-McMaster shoulder pain expression archive database and achieved average mean square error (MSE) of 1.53 and Pearson correlation coefficient (PCC) of 0.79 using leave-one-subject-out cross validation. Acquired results prove the superiority of dynamic facial features compared to the static ones in pain intensity determination applications. (More)

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Paper citation in several formats:
Zebarjadi, N. and Alikhani, I. (2017). Static and Dynamic Approaches for Pain Intensity Estimation using Facial Expressions. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF; ISBN 978-989-758-213-4; ISSN 2184-4305, SciTePress, pages 291-296. DOI: 10.5220/0006141502910296

@conference{healthinf17,
author={Niloufar Zebarjadi. and Iman Alikhani.},
title={Static and Dynamic Approaches for Pain Intensity Estimation using Facial Expressions},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF},
year={2017},
pages={291-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006141502910296},
isbn={978-989-758-213-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF
TI - Static and Dynamic Approaches for Pain Intensity Estimation using Facial Expressions
SN - 978-989-758-213-4
IS - 2184-4305
AU - Zebarjadi, N.
AU - Alikhani, I.
PY - 2017
SP - 291
EP - 296
DO - 10.5220/0006141502910296
PB - SciTePress