A joint deep neural network model for pain recognition from face

Bargshady, Ghazal ORCID: https://orcid.org/0000-0002-2557-7928 and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556 and Zhou, Xujuan and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Whittaker, Frank and Wang, Hua (2019) A joint deep neural network model for pain recognition from face. In: 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019), 23-25 Feb 2019, Singapore.


Official URL: http://www.icccs.org/

Abstract

Abstract—Pain is a primary symptom of diseases and an indicator of a patients’ health status. Effective management of pain is important for patient treatment and well-being. There are some traditional self-reported methods for pain assessment, and automatic pain detection systems using facial expressions are developing rapidly; these offer the potential for more efficient, convenient and cost-effective pain management. In this paper, a joint deep neural network model is proposed to classify pain intensity in four categories from facial images. This study used two different Recurrent Neural Networks RNN), which were pre-trained with Visual Geometric Group Face Convolutional Neural Network (VGGFace CNN) and then joined together as a network to estimate pain intensity levels. The UNBC-McMaster Shoulder Pain database was used to train and test the proposed algorithm. As a contribution o knowledge, this paper provides new information regarding he performance of a hybrid, joint deep learning algorithm or pain multi-classification in facial images.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2019 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.
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Date Deposited: 24 Apr 2019 06:22
Last Modified: 05 Jul 2019 05:02
Uncontrolled Keywords: expressions; pain recognition; deep convolutional network; transfer learning; computer vision
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460999 Information systems not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/36314

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