This thesis present the tree structure detector of Haar-like feature for face detector, a training speedup tick, and the feature scale correct method. In the feature training of Adaboost algorithm, the process adapt much more features for complex positive data, and the additional features are not efficient to the final result. We propose the tree structure to improve the data path. The distributions of trained distinct data are separated as high and low, and the difference can be also observed on the value of stage detector. Tree nodes are used to separate data by their detector values. In next stage, the data with difference will be trained independently. It decreases the production of ineffective features, and its data path can be a support of intra data classify. In my experiments, I found a scaled haar-like feature can’t fetch a pixel with float point position, and the results of detection are worse to the non-scaled feature. The scaled feature need a proper approximation. The Scaled Feature Correction method we proposed improve the detect rate of scaled feature. It also let the gather of negative samples become more easily when we want to train a classifier with generalization.