A new face detector with very high accuracy and real-time speed for mobile phone is introduced. The method achieves the fastest speed and the highest accuracy compared with other similar methods. A series of ideas are proposed in order to accelerate detection speed of the traditional Adaboost detector. First of all, the threshold for weak classifier is learned based on a new multiple instance pruning method regarding not only positive samples but also negative samples, by which, weak classifier is able to reject background more efficiently. Then, a coarse-to-fine scan is applied. Coarse scan is used to find possible face location, and the fine scan refines the face location and rejects false alarms. We further improve the speed of multi-scale face detection by introducing two different template sizes for detector training. By which, the smaller faces can be rapidly detected and the high performance is kept for larger faces. The proposed method is evaluated on public dataset FDDB, the result shows competitive performance against all Adaboost based methods. The method has been implemented on mobile phone and the speed is superior to all competitors.