Ting, JieJieTingYang, Yi ChengYi ChengYangLI-CHEN FUCHU-LIN TSAICHIEN-HUA HUANG2022-03-162022-03-162021-01-019781665443371https://scholars.lib.ntu.edu.tw/handle/123456789/597676https://api.elsevier.com/content/abstract/scopus_id/85125878665https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125878665&doi=10.1109%2fICMLA52953.2021.00177&partnerID=40&md5=429cb341857070f106a2b93db21e6728The pain intensity level is one of the properties to know the patients' status. In this work, we focus on estimating each individual's self-reported pain metric called Visual Analogue Scale (VAS), which is considered the standard gold measurement in the triage system. The VAS pain score is highly subjective, and its range may vary significantly among different patients. To tackle these issues, we designed an end-to-end training deep learning model for the automatic measurement of VAS based on video facial recognition. We proposed a novel loss method named Distance Ordering. By using Distance Ordering, we can extract the features with ordinal meaning according to the ordinal relationship of pain intensity levels. Experimental results on the UNBC-McMaster Pain Archive Database benchmark show that the model we designed outperforms the other previous works and achieves the state-of-the-art performance with Mean Square Error (MSE), Mean Absolute Error (MAE), Intra-class correlation (ICC), and Pearson coefficient correlation (PCC). Also, the ablation studies demonstrate that our approach can improve the VAS estimation.Deep learning | Facial Expression | Pain | Video Recognition | Visual Analogue Scaleconference paper10.1109/ICMLA52953.2021.001772-s2.0-85125878665