A Joint Deep Neural Network Model for Pain Recognition from Face

Ghazal Bargshady · Jeffrey Soar · Xujuan Zhou · Ravinesh Chand Deo · Frank Whittaker · Hua Wang

50 citations0/0 files storedInternational Conference on Computer and Communication Systems (ICCCS)

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

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.

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References

2011Painful data: The UNBC-McMaster shoulder pain expression archive database 2017Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification 2012Continuous Pain Intensity Estimation from Facial Expressions 2016Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video 2018Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities 2015Facial expression recognition based on transfer learning from deep convolutional networks 2013Pain detection through shape and appearance features 2017Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation

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2020The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space 2020Two-Stream Attention Network for Pain Recognition from Video Sequences

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