We perform face tracking and pose estimation jointly within a mixed-state particle filter framework. Previous methods
often used generative appearance models and naive prior state transition. We propose to use discriminating models,
Adaboosted face detectors, to both measure observations and provide information for the proposal distribution which is
combined with detection responses and prior transition model. Due to pose continuity, faces between discrete poses can
be detected by neighboring pose-specific detectors and serve as importance samples. Thus continuous poses are obtained
instead of discrete poses. Experiments show that our method is robust to large location and pose changes, partial
occlusions and expressions.