In the classical CONDENSATION for object tracking, a prior transition probability, i.e., first or second order AR dynamic model is used to propagate the particles. However, it results in poor performance frequently. In order to propagate the particles efficiently, an improved CONDENSATION face tracking algorithm based on mean-shift drift is proposed. The approach uses the efficient mean shift tracker to attain coarse location of face target, then uses these results to determine the deterministic drift, finally propagates the particles with a small stochastic diffusion added. Because sampling via the proposed method can always make particles cluster around the true state region, the particles efficiency can be improved greatly. The experimental results of face tracking demonstrate that the performance of proposed algorithm is superior to the standard CONDENSATION.