[1]邹修明,孙怀江,杨 赛.基于概率隐含语义分析模型的人脸识别算法[J].南京理工大学学报(自然科学版),2016,40(05):594.[doi:10.14177/j.cnki.32-1397n.2016.40.05.015]
 Zou Xiuming,Sun Huaijiang,Yang Sai.New face recognition algorithm based on probabilistic latent semantic analysis[J].Journal of Nanjing University of Science and Technology,2016,40(05):594.[doi:10.14177/j.cnki.32-1397n.2016.40.05.015]
点击复制

基于概率隐含语义分析模型的人脸识别算法
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年05期
页码:
594
栏目:
出版日期:
2016-10-30

文章信息/Info

Title:
New face recognition algorithm based on probabilistic latent semantic analysis
文章编号:
1005-9830(2016)05-0594-05
作者:
邹修明12孙怀江1杨 赛3
1.南京理工大学 计算机科学与工程学院,江苏 南京 210094; 2.淮阴师范学院 计算机科学与 技术学院,江苏 淮安 223300; 3.南通大学 电气工程学院,江苏 南通 226019
Author(s):
Zou Xiuming 12Sun Huaijiang 1Yang Sai 3
1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.School of Computer Science and Technology,Huaiyin Normal University,Huaian 223300,China; 3.School of Electrical Engineering,Nantong Univers
关键词:
人脸识别 概率隐含语义分析 词袋模型
Keywords:
face recognition probabilistic latent semantic analysis bag-of-word models
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2016.40.05.015
摘要:
该文提出一种基于概率隐含语义分析(PLSA)的新的人脸识别算法。首先建立人脸图像的词袋模型,然后使用概率隐含语义分析模型得到词袋特征在隐含主题空间中的分布,并将其作为人脸图像的最终语义特征表示,最后采用支持向量机(SVM)对人脸进行识别。Multi-PIE和人脸识别竞赛(FRGC)数据库上的实验结果表明,该文方法的性能优于目前多个人脸识别方法。
Abstract:
A new face recognition algorithm based on the probabilistic latent semantic analysis(PLSA)is proposed.Firstly the bag-of-word model of the face image is contructed.Then the PLSA model is used to get the distribution of the bag-of -word model in the latent topic space as the final semantic features of the face image.Lastly images are classified and recognized by the support vector machine(SVM).Experimental results on the multi-PIE and the face recognition grand challenge(FRGC)datasets show that the method gets higher classification accuracies than the current methods for the face recognition.

参考文献/References:

[1] Turk M,Pentland A.Eigenfaces for recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71-86.
[2]Belhumeur P N,Hespanha J P,Kriegman D J.Eigenfaces vs Fisherfaces:Recognition using class specific linear projection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[3]杨章静,刘传才,顾兴健,等.依概率分类的保持投影及其在人脸识别中的应用[J].南京理工大学学报,2013,37(1):7-11.
Yang Zhangjing,Liu Chuancai,Gu Xingjian,et al.Probabilistic classification preserving projections and its application to face recognition[J].Journal of Nanjing University of Science and Technology,2013,37(1):7-11.
[4]He X F,Yan S,Hu Y,et al.Face recognition using Laplacianfaces[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[5]王建国,杨万扣,郑宇杰,等.一种基于ICA和模糊LDA的特征提取方法[J].模式识别与人工智能,2008(6):819-823.
Wang Jianguo,Yang Wankou,Zheng Yujie,et al.A feature extraction method based on ICA and fuzzy LDA[J].Pattern Recognition and Artificial Intelligence,2008(6):819-823.
[6]Schölkopf B,Smola A J,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural computation,1998,10:1299-1319.
[7]Mika S,Rätsch G,Weston J,et al.Fisher discriminant analysis with kernels[J].Neural Networks for Signal Processing,1999,9:41-48.
[8]Wright J,Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-226.
[9]Zhang L,Yang M,Feng X.Sparse representation or collaborative representation:Which helps face recognition?[C]//Proceedings of the 2011 Interna-tional Conference on Computer Vision.Barcelona,Spain:IEEE Computer Society,2011:471-478.
[10]Yang M,Zhang L,Yang J,et al.Robust sparse coding for face recognition[C]//Proceedings of the 24th International Conference on Computer Vision and Pattern Recognition.Colorado Springs,US:IEEE Computer Society,2011,42(7):625-632.
[11]Yang M,Zhang L,Feng X C,et al.Sparse representation based Fisher discrimination dictionary learning for image classification[J].International Journal of Computer Vision,2014,109(3):209-232.
[12]Chen S,Liu J,Zhou Z.Making FLDA applicable to face recognition with one sample per person[J].Pattern Recognition,2004,37(7):1553-1555.
[13]Li Zisheng,Imai Jun-ichi,Masahide Kaneko.Robust face recognition using block-based bag of words[C]//Proceedings of the 26th International Conference on Pattern Recognition.Istanbul,Turkey:IEEE,2010:1285-1288.
[14]Zhen Cui,Wen Li,Dong Xu,et al.Fusing robust face region descriptors via multiple metric learning for face recognition in the wild[C]//Proceedings of the 26th International Conference on Computer Vision and Pattern Recognition.Portland,US:IEEE,2013:3554-3561.
[15]Hofmann T.Unsupervised learning by probabilistic latent semantic analysis[J].Machine Learning,2001,42(1):177-196.

相似文献/References:

[1]程勇,赵春霞,王欢,等.基于NSCT和SQI的光照不变量及人脸识别[J].南京理工大学学报(自然科学版),2010,(04):425.
 CHENG Yong,ZHAO Chun-xia,WANG Huan,et al.Illumination Invariant and Face Recognition Based on NSCT and SQI[J].Journal of Nanjing University of Science and Technology,2010,(05):425.
[2]范燕,於东军,宋晓宁,等.镜像基函数下过渡投影子空间人脸特征抽取算法[J].南京理工大学学报(自然科学版),2012,36(06):0.
 FAN Yan,YU Dong jun,SONG Xiao ning,et al.Face Feature Extraction Approach of Projective Transition Subspace Based on Basis Function of Mirror Symmetry[J].Journal of Nanjing University of Science and Technology,2012,36(05):0.
[3]杨章静,刘传才,顾兴健,等.依概率分类的保持投影及其在人脸识别中的应用[J].南京理工大学学报(自然科学版),2013,37(01):7.
 Yang Zhangjing,Liu Chuancai,Gu Xingjian,et al.Probabilistic classification preserving projections and its application to face recognition[J].Journal of Nanjing University of Science and Technology,2013,37(05):7.
[4]黄修武,郭跃飞,杨静宇.基于代数方法的图像特征抽取和识别[J].南京理工大学学报(自然科学版),1998,(01):5.
 Huang Xiuwu Guo Yuefei Yang Jingyu.Image Feature Extraction and Recognition Based on Algebraic Method[J].Journal of Nanjing University of Science and Technology,1998,(05):5.
[5]张琳梅,张雪峰.曲波变换和独立分量分析的人脸识别[J].南京理工大学学报(自然科学版),2017,41(01):74.[doi:10.14177/j.cnki.32-1397n.2017.41.01.010]
 Zhang Linmei,Zhang Xuefeng.Face recognition based on curvelet transform and independentcomponent analysis[J].Journal of Nanjing University of Science and Technology,2017,41(05):74.[doi:10.14177/j.cnki.32-1397n.2017.41.01.010]

备注/Memo

备注/Memo:
收稿日期:2016-01-15 修回日期:2016-09-07
基金项目:国家自然科学基金(61402192); 江苏省高校自然科学基金重大资助项目(15KJA460004)
作者简介:邹修明(1968-),男,博士生,副教授,主要研究方向:模式识别、机器学习和生物信息学,E-mail:brightzou@126.com; 通讯作者:孙怀江(1968-),男,博士,教授,主要研究方向:图形图像技术与应用,机器学习与数据挖掘,认知计算,E-mail:sunhuaijiang@njust.edu.cn。
引文格式::邹修明,孙怀江,杨赛.基于概率隐含语义分析模型的人脸识别算法[J].南京理工大学学报,2016,40(5):594-598.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-10-30