Ensemble neural network approach detecting pain intensity from facial expressions

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

69 citations0/0 files storedArtificial Intelligence in Medicine

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Emotion and Mood Recognition Advanced Neural Network Applications Face recognition and analysis

Abstract

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2011Painful data: The UNBC-McMaster shoulder pain expression archive database 2017Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification 2010Automatically Detecting Pain in Video Through Facial Action Units 2019Automatic Recognition Methods Supporting Pain Assessment: A Survey 2020Enhanced deep learning algorithm development to detect pain intensity from facial expression images 2016Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video 2017Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions

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2023Using AI to Detect Pain through Facial Expressions: A Review

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