Pain Facial Expression Recognition from Video Sequences Using Spatio temporal Local Binary Patterns and Tracking Fiducial Points
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.