Pain detection from facial expressions using domain adaptation technique

• Published in: Pattern analysis and applications : PAA Vol. 25; no. 3; pp. 567 - 574
• Main Authors: Rathee, Neeru, Pahal, Sudesh, Sheoran, Poonam
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2022; Springer Nature B.V
• Subjects: Adaptation; Algorithms; Cognitive tasks; Computer Science; Deep learning; Image acquisition; Machine learning; Original Article; Pain; Pain management; Pattern Recognition; Special Issue on Computer Vision and Machine Learning for Healthcare Applications; Deep learning; Real pain intensity detection; Model agnostic meta-learning; Facial expression analysis
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-021-01025-4

Pain management is gaining the attention of clinical practitioners to relieve patients from pain in an effective manner. Pain management is primarily dependent on pain measurement. Researchers have proposed various techniques to measure pain from facial expressions improving the accuracy and efficiency of the traditional pain measurement such as self-reporting and visual analog scale. Developments in the field of deep learning have further enhanced the pain assessment technique. Despite of the state of the art performance of deep learning algorithms, adaptation to new subjects is still a problem due to availability of a few samples of the same. Authors have addressed this issue by employing a model agnostic meta-learning algorithm for pain detection and fast adaptation of the trained algorithm for new subjects using only a few labeled images. The model is pre-trained with labeled images of subjects with five pain levels to acquire meta-knowledge in the presented work. This meta-knowledge is then used to adapt the model to a new learning task in the form of a new subject. The proposed model is evaluated on a benchmark dataset, i.e., UNBC McMaster pain archive database. Experimental results show that the model can be very easily adapted to new subjects with the accuracy of 96 % and 98 % for 1-shot and 5-shot learning respectively, proving the potential of the proposed algorithm for clinical use.