Automatic Pain Assessment with Facial Activity Descriptors

Abstract

Pain is a primary symptom in medicine, and accurate assessment is needed for proper treatment. However, today's pain assessment methods are not sufficiently valid and reliable in many cases. Automatic recognition systems may contribute to overcome this problem by facilitating objective and continuous assessment. In this article we propose a novel feature set for describing facial actions and their dynamics, which we call facial activity descriptors. We apply them to detect pain and estimate the pain intensity. The proposed method outperforms previous state-of-the-art approaches in sequence-level pain classification on both, the BioVid Heat Pain and the UNBC-McMaster Shoulder Pain Expression database. We further discuss major challenges of pain recognition research, benefits of temporal integration, and shortcomings of widely used frame-based pain intensity ground truth.