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一种基于半监督学习的2DPCA人脸识别方法
  • ISSN号：1000-1565
  • 期刊名称：河北大学学报(自然科学版)
  • 时间：2013.7.7
  • 页码：413-419
  • 分类：TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]河北大学数学与计算机学院,河北保定071002
  • 相关基金：国家自然科学基金资助项目（61073121）;河北省自然科学基金资助项目（F2012201014）
  • 相关项目：不确定支持向量机及其应用
作者： 李凯|徐治平|
关键词： 人脸识别, 半监督学习, 二维主成分分析法(2DPCA), 特征提取, face recognition, semi-supervised learning, two-dimensional principal component analysis （2DPCA） , feature extraction
中文摘要：

结合半监督学习中的自学习技术以及二维主成分分析（two-dimensional principal componentanalysis-2DPCA1）方法，提出了一种基于半监督学习的人脸识别方法．在二维主成分分析的基础上，利用少量具有类别标签的样本训练分类器，然后利用半监督学习中的自学习技术，对未知类别标签的人脸样本进行分类，并将具有高置信度的人脸样本加入到训练集中，以此增加训练集中的人脸样本数量．在ORL人脸库和Yale人脸库的实验结果，表明了提出方法的有效性．

英文摘要：

By combining self-training method of the semi-supervised learning with two-dimensional principal component analysis （2DPCA）, a semi-supervised learning based face recognition method was proposed. On the basis of two-dimensional principal component analysis, few labeled samples were used to obtain classifier. Then unlabeled samples were classified through the classifier. And according to the selftraining method of semi-supervised learning, the face samples with the highest confidence were added to the training set in order to increase the number of face samples in training set. Experimental results on ORL face database and Yale face database showed the effectiveness of the presented method.

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期刊信息
  • 《河北大学学报：自然科学版》
  • 北大核心期刊（2011版）
  • 主管单位:河北省教育厅
  • 主办单位:河北大学
  • 主编：傅广生
  • 地址：保定市五四东路180号
  • 邮编：071002
  • 邮箱：hbdxxbz@hbu.edu.cn
  • 电话：0312-5079413
  • 国际标准刊号：ISSN：1000-1565
  • 国内统一刊号：ISSN：13-1077/N
  • 邮发代号:18-257
  • 获奖情况:
  • 2008年10月荣获第二届中国高校优秀科技期刊奖,2008年荣获2006-2007年度河北省优秀科技期刊奖,2009年8月被河北省教育厅命名为2004-2008年度河北...,2009年8月在中国北方优秀期刊评选活动中被评为"中...,2009年10月荣获2009年全国高校科技期刊优秀编辑质量奖,2010年10月荣获第三届中国高校优秀科技期刊奖
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国化学文摘（网络版）,美国数学评论（网络版）,美国剑桥科学文摘,英国动物学记录,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,英国英国皇家化学学会文摘
  • 被引量:5593