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Patient pain can be detected highly reliably from facial expressions using aset of facial muscle-based action units (AUs) defined by the Facial ActionCoding System (FACS). A key characteristic of facial expression of pain is thesimultaneous occurrence of pain-related AU combinations, whose automateddetection would be highly beneficial for efficient and practical painmonitoring. Existing general Automated Facial Expression Recognition (AFER)systems prove inadequate when applied specifically for detecting pain as theyeither focus on detecting individual pain-related AUs but not on combinationsor they seek to bypass AU detection by training a binary pain classifierdirectly on pain intensity data but are limited by lack of enough labeled datafor satisfactory training. In this paper, we propose a new approach that mimicsthe strategy of human coders of decoupling pain detection into two consecutivetasks: one performed at the individual video-frame level and the other atvideo-sequence level. Using state-of-the-art AFER tools to detect single AUs atthe frame level, we propose two novel data structures to encode AU combinationsfrom single AU scores. Two weakly supervised learning frameworks namelymultiple instance learning (MIL) and multiple clustered instance learning(MCIL) are employed corresponding to each data structure to learn pain fromvideo sequences. Experimental results show an 87% pain recognition accuracywith 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expressiondataset. Tests on long videos in a lung cancer patient video datasetdemonstrates the potential value of the proposed system for pain monitoring inclinical settings.

Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning