Pain expression assessment based on a locality and identity aware network

Abstract

In clinical medicine, the pain feeling is a significant indicator for the medical condition of patients. Of late, automatic pain assessment methods have received more and more interests. Many researchers proposed corresponding methods and achieved impressive results. However, they always ignore the locality and individual differences of painful expression. Therefore, a locality and identity aware network (LIAN) for pain assessment is presented here. Concretely, for the locality characteristic, a locality aware module consisting of a two-branch structure, feature and attention branches, is presented. The former learns pain features by a deep network, while the latter guides the pain features to focus on the discriminational regions of pain. As for the individual differences, an identity aware module with a multi-task method is proposed to represent identity-related information to achieve identity-invariant pain assessment. Extensive experiments on public databases show the superiority of LIAN in pain assessment.