Pain Expression Recognition Using Occluded Faces

Abstract

Frequent pain monitoring in intensive care unit (ICU) settings has been shown to have achieved improvement in patient outcomes. Specifically, the nursing staff is required to assess the pain of the patients approximately every four hours, and make necessary pain medication adjustments. Shortage and overburdening of nursing staff is well acknowledged in many hospital settings worldwide. In this paper, we present a preliminary study on automatic recognition of facial expression of pain in ICU settings. There has been considerable work on computer based pain recognition for settings beyond the ICU, wherein, usually the entire face of the person is visible without occlusions. The ICU setting, however, brings along unique challenges for recognizing facial expression of pain – the patient’s face is often covered by a respirator mask, the face is partially occluded by accessories attached to the respirator, and the patient is occasionally in a transient state of consciousness. In this paper we investigate the use of computer vision techniques for recognizing pain from partially visible faces. To establish a proof-of-concept, we simulate the occlusions most likely to happen in an ICU setting by masking out the nose, cheeks, and mouth regions, and use only a narrow band around the eyes of the patient as an input to our method. We generate these simulated occlusions using data from the UNBC-McMaster pain expression archive −a previously used dataset for pain expression recognition based on fully visible faces. We investigate multiple feature representations by extracting features only on the basis of the eye-region and train classifiers to detect the presence of pain using leave-one-person-out cross validation. Our results suggest a potential viability of automatic pain monitoring in the ICU settings involving face occlusions.