Rapid face detection using an automatic distributing detector based on fuzzy logic

To improve the efficiency of a face detector, this paper presents an automatic distributing detector (ADD) based on the fuzzy theory to improve the performance of face detection. The main contributions lie in:l) A new Haar-like feature representation based on the fuzzy membership function is proposed, 2)The entropy of feature set is employed as choice criteria to select weak classifiers, 3) The AdaBoost algorithm is used to train weak classifiers, and 4)The distributor which can dynamically select stronger classifiers is constructed. The experiment results show that the proposed method not only determines rapidly the sub-window which contains the human face, but also tune the classifier dynamically to adaptive new samples. The accuracy and speed of our method are also promoted comparison with the state-of-art detectors. On the other hand, as for the image sub-window which is like face, according to the value of membership function, distributor can dynamically select the remaining stronger classifiers to determine. This detector can effectually improve detection speed and has better detection performance.