@inproceedings{dd0561b4594f497786552dfd38fe9d90,
title = "A joint deep neural network model for pain recognition from face",
abstract = "Pain is a primary symptom of diseases and an indicator of a patients{\textquoteright} 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",
author = "Ghazal Bargshady and Jeffrey Soar and Xujuan Zhou and Deo, {Ravinesh C.} and Frank Whittaker and Hua Wang",
note = "Funding Information: ACKNOWLEDGMENT The lead author received financial support from the Australian Research Council (ARC) linkage LP150100673 - Privacy Preserving Data Sharing in Electronic Health Environment, from Nexus eCare and from the University of Southern Queensland (USQ) International Higher Degree Research (HDR) Scholarship. Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 ; Conference date: 23-02-2019 Through 25-02-2019",
year = "2019",
month = feb,
doi = "10.1109/CCOMS.2019.8821780",
language = "English",
isbn = "9781728113234",
series = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "52--56",
editor = "Yang Xiao and Nobuo Funabiki",
booktitle = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",
address = "United States",
}