A joint deep neural network model for pain recognition from face
Paper
Paper/Presentation Title | A joint deep neural network model for pain recognition from face |
---|---|
Presentation Type | Paper |
Authors | Bargshady, Ghazal, Soar, Jeffrey, Zhou, Xujuan, Deo, Ravinesh, Whittaker, Frank and Wang, Hua |
Journal or Proceedings Title | Proceedings of the 4th IEEE International Conference on Computer and Communication Systems (ICCS 2019) |
Journal Citation | pp. 52-56 |
Article Number | 8821779 |
Page Range | 52-56 |
Number of Pages | 5 |
Year | 2019 |
Place of Publication | Singapore |
ISBN | 9781728113227 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CCOMS.2019.8821779 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/8821779 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/8811523/proceeding |
Conference/Event | 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019) |
Event Details | 4th IEEE International Conference on Computer and Communication Systems (ICCCS 2019) Parent IEEE International Conference on Computer and Communications Event Date 23 to end of 25 Feb 2019 Event Location Singapore |
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 to knowledge, this paper provides new information regarding the performance of a hybrid, joint deep learning algorithm for pain multi-classification in facial images. |
Keywords | Computer vision; Deep convolutional network; Facial expressions; Pain recognition; Transfer learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460305. Image and video coding |
Funder | Australian Research Council |
Byline Affiliations | University of Southern Queensland |
University of Southern Queensland | |
Nexus eCare, Australia | |
Victoria University |
https://research.usq.edu.au/item/v8890/a-joint-deep-neural-network-model-for-pain-recognition-from-face
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