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Automatic pain recognition is paramount for medical diagnosis and treatment.The existing works fall into three categories: assessing facial appearancechanges, exploiting physiological cues, or fusing them in a multi-modal manner.However, (1) appearance changes are easily affected by subjective factors whichimpedes objective pain recognition. Besides, the appearance-based approachesignore long-range spatial-temporal dependencies that are important for modelingexpressions over time; (2) the physiological cues are obtained by attachingsensors on human body, which is inconvenient and uncomfortable. In this paper,we present a novel multi-task learning framework which encodes both appearancechanges and physiological cues in a non-contact manner for pain recognition.The framework is able to capture both local and long-range dependencies via theproposed attention mechanism for the learned appearance representations, whichare further enriched by temporally attended physiological cues (remotephotoplethysmography, rPPG) that are recovered from videos in the auxiliarytask. This framework is dubbed rPPG-enriched Spatio-Temporal Attention Network(rSTAN) and allows us to establish the state-of-the-art performance ofnon-contact pain recognition on publicly available pain databases. Itdemonstrates that rPPG predictions can be used as an auxiliary task tofacilitate non-contact automatic pain recognition.

Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction