User's face localization and head pose estimation play an essential role in the application of face-to-face human-computer interactive systems. In this paper, we formulate pose localization in a Bayesian framework by particle sampling. Proposal distribution fusing multiple cues such as color, shape and motion is defined to realize pose localization and tracking, which effectively enhances the performance of particle sampling. Experiment results on static images and real videos show the efficiency of our algorithm.