Face Gabor Feature Selection Based on Adaboost

Abstract

Through the extraction of face image Gabor feature, combined with Adaboost for face recognition. According to the characteristics of high dimension Gabor, redundancy is large, the introduction of Adaboost algorithm for feature selection to reduce the dimensions of feature vector, for a large number of Gabor feature selection. At the same time ,using a single positive sample set and several negative sample sets for training method to construct a strong classifier cascade classifier. Testing results in the Yale library proves the validity of the method.