A robust and automatic face tracker dedicated to broadcast videos

Because of their lack of rules, general broadcast videos are more difficult to analyze than news or sport videos. To retrieve human interventions in this context, a robust face tracker is needed. The approach we investigate for face tracking combines three main modules that are a face detector, a region-based tracker and an eye tracker. The region-based tracker relies on a robust parametric motion estimation technique. The eye tracker is based on a Kalman filter. The analysis of the coherence of the trackers output provides an efficient way to detect profile positions and tracking errors. We have thus defined an entirely automatic tracker, able to manage several appearing/disappearing faces, without any a priori knowledge on the image sequence. Experimental results on broadcast videos demonstrate its efficiency to deal with large and rapid motions, occlusions and faces in profile position.