PAIN DETECTION THROUGH SHAPE AND APPEARANCE FEATURES

In this paper we are proposing a novel computer vision system that can recognize expression of pain in videos by analyzing facial features. Usually pain is reported and recorded manually and thus carry lot of subjectivity. Manual monitoring of pain makes difficult for the medical practitioners to respond quickly in critical situations. Thus, it is desirable to design such a system that can automate this task. With our proposed model pain monitoring can be done in real-time without any human intervention. We propose to extract shape information using pyramid histogram of orientation gradients (PHOG) and appearance information using pyramid local binary pattern (PLBP) in order to get discriminative representation of face. We tested our proposed model on UNBC-McMaster Shoulder Pain Expression Archive Database and recorded results that exceeds state-of-the-art.