[1]周晓彦,郑文明,辛明海.基于多标签核判别分析的人脸身份与性别识别方法[J].东南大学学报(自然科学版),2014,44(5):912-916.[doi:10.3969/j.issn.1001-0505.2014.05.007]
 Zhou Xiaoyan,Zheng Wenming,Xin Minghai.Face and gender recognition method based on multi-label kernel discriminant analysis[J].Journal of Southeast University (Natural Science Edition),2014,44(5):912-916.[doi:10.3969/j.issn.1001-0505.2014.05.007]
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基于多标签核判别分析的人脸身份与性别识别方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
44
期数:
2014年第5期
页码:
912-916
栏目:
计算机科学与工程
出版日期:
2014-09-20

文章信息/Info

Title:
Face and gender recognition method based on multi-label kernel discriminant analysis
作者:
周晓彦12郑文明3辛明海3
1南京信息工程大学江苏省气象探测与信息处理重点实验室, 南京 210044; 2南京信息工程大学江苏省气象传感网技术工程中心, 南京 210044; 3东南大学儿童发展与学习科学教育部重点实验室, 南京 210096
Author(s):
Zhou Xiaoyan12 Zheng Wenming3 Xin Minghai3
1Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Jiangsu Technology and Engineering Center of Meteorological Sensor Network, Nanjing University of Information Science and Technology, Nanjing 210044, China
3Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China
关键词:
多标签核判别分析 维数约简 人脸识别 性别识别
Keywords:
multi-label kernel discriminant analysis dimensionality reduction face recognition gender recognition
分类号:
TP391.41
DOI:
10.3969/j.issn.1001-0505.2014.05.007
摘要:
为解决多标签线性判别分析(MLDA)方法在非线性维数约简方面的局限性,提出了一种多标签核判别分析(MKDA)方法,并将其用于人脸的身份与性别识别中.该方法的基本思想是通过非线性映射将训练样本从输入空间映射到高维核特征空间中,并在该特征空间中进行基于MLDA的数据降维.在身份和性别识别中,首先采用MKDA方法对人脸图像特征向量进行降维,获取判别特征矢量集;其次,为每幅人脸图像赋予一个表征身份和性别的多标签类别矢量;最后,采用减秩回归模型(RRR)描述判别特征矢量与多标签类别矢量之间的回归关系,并利用该模型进行未知人脸的身份和性别识别.AR人脸数据库上的实验结果表明:在人脸身份和性别识别中,MKDA方法的识别率高于传统核判别分析(KDA)方法.
Abstract:
A multi-label kernel discriminant analysis(MKDA)method is proposed to overcome the limitation of multi-label linear discriminant analysis(MLDA)on nonlinear dimensionality reduction, and applied to the recognition of face and gender. The basic idea of the MKDA method is to map the training data samples from the input data space to a high-dimensional kernel feature space via a nonlinear mapping and then to perform data reduction based on the MLDA method in the feature space. During the recognition of face and gender, the dimensionality of the face image feature vectors is firstly reduced by using the MKDA method and a set of discriminative feature vector set is obtained. Then, a multi-label class vector indicating the class membership of face and gender is assigned to each face image. Finally, a reduced-rank regression(RRR)model is built to describe the relationship between the discriminative facial feature vectors and multi-label class vectors, and is applied to the face and gender recognition of an unknown face image. The experimental results on AR face database show that the recognition rates of the MKDA method are higher than those of the traditional kernel discriminant analysis(KDA)in face and gender recognition.

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备注/Memo

备注/Memo:
收稿日期: 2013-12-28.
作者简介: 周晓彦(1980—),女,博士,讲师,xiaoyan-zhou@nuist.edu.cn.
基金项目: 国家自然科学基金资助项目(61201444,61231002)、教育部博士点基金资助项目(20120092110054)、江苏省自然科学基金资助项目(BK20130020)、江苏省高校自然科学基础研究自筹经费资助项目(08KJD520009)、江苏高校优势学科建设工程资助项目.
引用本文: 周晓彦, 郑文明, 辛明海.基于多标签核判别分析的人脸身份与性别识别方法[J].东南大学学报:自然科学版,2014,44(5):912-916. [doi:10.3969/j.issn.1001-0505.2014.05.007]
更新日期/Last Update: 2014-09-20