%0 Journal Article
%T Face Samples Expanding Based on the GA Re-Sampling<br>基于遗传算法重采样的人脸样本扩张
%A CHEN Jie
%A CHEN Xi-Lin
%A GAO Wen
%A <br>陈杰
%A 陈熙霖
%A 高文
%J 软件学报
%D 2005
%I 
%X Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. All of the statistical methods suffer from this problem. In this paper, a genetic algorithm (GA) based method to swell face database through re-sampling from existing faces is presented. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, the expanded database is used to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be speeded up efficiently by the proposed methods.
%K face detection
%K genetic algorithm
%K SnoW (sparse network of winnow)
%K AdaBoost<br>人脸检测
%K 遗传算法
%K SnoW(sparse
%K network
%K of
%K winnow)
%K AdaBoost
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=132D840B141EB45A&yid=2DD7160C83D0ACED&vid=7801E6FC5AE9020C&iid=708DD6B15D2464E8&sid=B2F4AE6815C8FC11&eid=8080149E52358D01&journal_id=1000-9825&journal_name=软件学报&referenced_num=3&reference_num=19