Fast Face Detection Based on Fuzzy Set Theory
Hongping Deng, Jian Zhang · 2009
In order to improve efficiency of face detector, fuzzy set theory is used in establishing distribution face detector. This detector trains the sample set by Haar-like feature and membership function, and selects appropriate weak classifiers through the feature setpsilas entropy and AdaBoost learning algorithm. Subsequently distribution face detector is established and tested on the MIT+CMU frontal face test set. The results show that the detector can rapidly eliminate the sub-window which is unlike a face through the front simple stronger classifiers; and that distributor can dynamically select the back stronger classifiers to determine whether it is true or not in terms of similar degree when the image sub-window looks like a face. This detector can effectually improve detection efficiency in the condition of detection performance reducing not too much.