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Most of the face anti-spoofing methods improve the generalization capability by adversarial domain adaptation via training the source and target domain data jointly. However, considering the data privacy, it is impractical in application. Hence, we propose a source data-free domain adaptative face anti-spoofing framework to optimize the network in the target domain without using labeled source data via modeling it into a problem of learning with noisy labels. To obtain more reliable pseudo labels, we propose dynamic images with the background to capture the motion divergences between real and attack faces. Nonetheless, fluctuations of predictions caused by noisy labels are still strong. Therefore, a filtering strategy is proposed to reduce the impact of noisy labels by self-ensemble, which combines prototype and progressive pseudo labels predicted by the source pre-trained model and target model respectively. The proposed approach shows promising generalization capability in several public-domains face anti-spoofing databases.