%0 Journal Article
%T Unified probabilistic models for face recognition from a single example image per person<br>Unified Probabilistic Models for Face Recognition from a Single Example Image per Person
%A Pin Liao
%A Li Shen
%A <br>PinLiao
%A LiShen
%J 计算机科学技术学报
%D 2004
%I 
%X This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.
%K pattern recognition
%K face recognition
%K Gaussian mixture model
%K classifier combination
%K unified probabilistic model<br>模式识别
%K 面貌识别
%K 高斯混合模型
%K 分类结合
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=2880F856CE20D8A85D36B52C2A34C05A&yid=D0E58B75BFD8E51C&vid=2A8D03AD8076A2E3&iid=38B194292C032A66&sid=2B25C5E62F83A049&eid=2B25C5E62F83A049&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=1&reference_num=43