A joint deep neural network model for pain recognition from face

Bargshady, Ghazal and Soar, Jeffrey and Zhou, Xujuan and Deo, Ravinesh C. 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—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: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
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 : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Socio-Economic Objective: 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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