local binary patterns

Monitoring the facial expressions of patients in clinical environments is a necessity in addition to vital sign monitoring. Pain monitoring of patients by facial expressions from video sequences eliminates the need for another person to accompany patients. In this paper, a novel approach is presented to monitor the expression of face and notify in case of pain using tracking fiducial points of face in video sequences and spatio-temporal Local Binary Patterns (LBPs) for eyes and eyebrows. The motion of eight fiducial points on facial features such as mouth, eyes, eyebrows are tracked by Lucas-Kanade algorithm and the movement angles are recorded in a feature vector which along with the spatio-temporal histogram of LBPs creates a concatenated feature vector. Spatio-temporal LBPs boost the proposed algorithm to capture minor deformations on eyes and eyebrows. The feature vectors are then compared and classified using the Chi-square similarity measure. Experimental results show that leveraging spatio-temporal LBPs improves the accuracy by 12% on STOIC database.