Probabilistic graphical detector fusion for localization of faces and facial parts

When face and facial part detectors encounter partial or complete occlusion, their output becomes increasingly noisy, making it difficult for such algorithms to tackle complex scenes: parts are only detected intermittently, false alarms become more prevalent, parts are mis-identified, and some parts are consistently missed altogether. In this paper we propose a remedy to this problem: probabilistic fusion of face and facial detectors. The method jointly improves the localization and tracking performance of face and facial parts detectors by modelling the spatial and temporal inter-dependences of these detectors in a probabilistic graphical model. Spatial modelling can be used to rectify mis-identification, filter out false detections and infer the presence of missed detections during partial occlusion. Temporal modelling improves face and facial tracking in scenarios involving completely missed detections or complete occlusion. Experimental results quantify substantial benefits in terms of improved precision, recall and F-measure both for the detection of facial parts and of the face.