Abstract: This paper analyses the feasibility that fuzzy set theoretics apply to face detection.It trains the sample set by Haar-like feature and membership function，and selects appropriate weak classifiers through the entropy of feature set and AdaBoost learning algorithm.Then distribution face detector is established.In the detection process，detector can rapidly wash out the sub-window which is unlike face through the front of simple stronger classifiers;for the sub-window which is like face，in terms of similar degree distributer can dynamically select the back of stronger classifiers to determine whether it is a face.This face detector has been tested on the MIT+CMU frontal face test set，the results show that it can effectually improve detection efficiency in the condition of detection performance reducing not too much.