In the light of the problems that at present in the main stream algorithms the integral regression method is still applied to all the feature points whereas the local structure information of human face is ignored, a novel cascaded regression structure is presented based on relative distance distribution and K-means clustering combined with face structure information on clustering facial landmarks and performing regression for each part respectively, the feature points locating of human face can be performed more accurately. In addition, the regression method is optimized to make robust parameter updated with efficiency. The paper carries out a thorough experiment on a face database (COFW) with block identification. The experiments demonstrate that the algorithm is notable in effect with regard to the application of feature points of a human face to the location, and the algorithm is greatly short in training time compared with Robust Cascaded Pose Regression and other stateoftheart methods and the testing speed is up to 220 fps, thus realizing realtime processing.