Automatic Detection of Pain from Spontaneous Facial Expressions

Meawad, F., Yang, S.-Y. and Loy, F. L. (2017) Automatic Detection of Pain from Spontaneous Facial Expressions. In: 19th ACM International Conference on Multimodal Interaction (ICMI 2017), Glasgow, Scotland, 13-17 Nov 2017, pp. 397-401. ISBN 9781450355438 (doi: 10.1145/3136755.3136794)

Abstract

This paper presents a new approach for detecting pain in sequences of spontaneous facial expressions. The motivation for this work is to accompany mobile-based self-management of chronic pain as a virtual sensor for tracking patients' expressions in real-world settings. Operating under such constraints requires a resource efficient approach for processing non-posed facial expressions from unprocessed temporal data. In this work, the facial action units of pain are modeled as sets of distances among related facial landmarks. Using standardized measurements of pain versus no-pain that are specific to each user, changes in the extracted features in relation to pain are detected. The activated features in each frame are combined using an adapted form of the Prkachin and Solomon Pain Intensity scale (PSPI) to detect the presence of pain per frame. Painful features must be activated in N consequent frames (time window) to indicate the presence of pain in a session. The discussed method was tested on 171 video sessions for 19 subjects from the McMaster painful dataset for spontaneous facial expressions. The results show higher precision than coverage in detecting sequences of pain. Our algorithm achieves 94% precision (F-score=0.82) against human observed labels, 74% precision (F-score=0.62) against automatically generated pain intensities and 100% precision (F-score=0.67) against self-reported pain intensities.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Meawad, Dr Fatma
• Authors: Meawad, F., Yang, S.-Y., and Loy, F. L.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the 19th ACM International Conference on Multimodal Interaction - ICMI 2017
• Publisher: ACM Press
• ISBN: 9781450355438
• Copyright Holders: Copyright © 2017 Association for Computing Machinery
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher