Pain Detection From Facial Expressions Based on Transformers and Distillation
| Autor: | Safaa El Morabit, Atika Rivenq |
|---|---|
| Přispěvatelé: | Université Polytechnique Hauts-de-France (UPHF), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC) 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC), May 2022, El Jadida, Morocco. pp.1-5, ⟨10.1109/ISIVC54825.2022.9800746⟩ |
| DOI: | 10.1109/ISIVC54825.2022.9800746⟩ |
| Popis: | International audience; Pain assessment is a challenging problem in the field of emotion recognition. Pain represents a complex emotion difficult to detect or to estimate its intensity. This is what makes automatic pain assessment playing an important role in clinical diagnosis. Taking into consideration that pain generally generates spontaneous facial behaviour, these facial expressions could be used to detect the presence of pain. As a matter of fact, previous researches used machine learning and deep learning either to detect pain or to estimate pain level. In this paper, we propose a fine-tuning of pre-trained data-efficient image transformers and distillation (Deit) for pain detection from facial expressions. The effectiveness of the proposed architecture is evaluated on two publicly available databases, namely UNBC McMaster Shoulder Pain and BioVid Heat Pain. The proposed approach achieved promising preliminary results compared to the state of the art. |
| Databáze: | OpenAIRE |
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