Learning appearance features for pain detection using the unbc-mcmaster shoulder pain expression archive database

Abstract

We propose a supervised approach to solve the task of automatic pain detection from facial expressions. A pain detection algorithm should be both robust to face pose and the identity of the face (identity bias). In order to achieve invariance to face pose, we use an Active Appearance Model (AAM) to warp all face images into frontal pose. The main contribution of our paper is a discriminative feature extractor that addresses identity bias by learning appearance features that separate pain-related factors from other factors, such as those related to the identity of the face. The system achieves state-of-the-art performance on the UNBC-McMaster Shoulder Pain Expression Archive Database.