Kernel scatter-difference based discriminant analysis for face recognition

被引:12
作者
Liu, QS [1 ]
Tang, XO [1 ]
Lu, HQ [1 ]
Ma, SD [1 ]
机构
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
来源
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 | 2004年
关键词
D O I
10.1109/ICPR.2004.1334241
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There are two problems with the Fisher Linear Discriminant Analysis (FLDA) for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that FLDA cannot efficiently describe complex nonlinear variations Of face images with illumination, pose and facial expression variations, due to its linear property. In this paper, a kernel scatter-difference based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference based discriminant rule is defined to analysis the data in F. The proposed method can not only produce nonlinear discriminant features in accordance with the principle of maximizing between-class scatter and minimizing within-class scatter, but also avoid the singularity problem of the within class scatter matrix. Experiments on the FERET database show an encouraging recognition performance of the new algorithm.
引用
收藏
页码:419 / 422
页数:4
相关论文
共 50 条
[31]   Bagging based efficient Kernel Fisher Discriminant Analysis for face recognition [J].
Li, Yi ;
Zhang, Baochang ;
Shan, Shiguang ;
Chen, Xilin ;
Gao, Wen .
18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS, 2006, :523-+
[32]   Kernel-based fisher minimum discriminant analysis and face recognition [J].
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China ;
不详 .
Xitong Fangzhen Xuebao, 2008, 20 (5516-5518+5522)
[33]   Color Face Images Recognition Based on Quaternion Kernel Discriminant Analysis [J].
Jing, Xiao-Yuan ;
Tang, Hui ;
Yao, Yong-Fang ;
Zhang, Ji-Wei .
2011 3RD WORLD CONGRESS IN APPLIED COMPUTING, COMPUTER SCIENCE, AND COMPUTER ENGINEERING (ACC 2011), VOL 4, 2011, 4 :53-+
[34]   Face Recognition based on Two-dimension Kernel Principal Component Analysis and Fuzzy Maximum Scatter Difference [J].
Zeng, Jie Xian ;
Wang, Wei ;
Tian, Jin Quan .
2015 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE), 2015, :351-357
[35]   Kernel relevance weighted discriminant analysis for face recognition [J].
Khalid Chougdali ;
Mohamed Jedra ;
Nouredine Zahid .
Pattern Analysis and Applications, 2010, 13 :213-221
[36]   Face recognition using kernel uncorrelated discriminant analysis [J].
Jiao, Licheng ;
Hu, Rui ;
Zhou, Weida ;
Gao, Yi .
ADVANCES IN MULTIMEDIA MODELING, PT 2, 2007, 4352 :415-+
[37]   Kernel relevance weighted discriminant analysis for face recognition [J].
Chougdali, Khalid ;
Jedra, Mohamed ;
Zahid, Nouredine .
PATTERN ANALYSIS AND APPLICATIONS, 2010, 13 (02) :213-221
[38]   An efficient reformative kernel discriminant analysis for face recognition [J].
Li, Jun-Bao ;
Pan, Jeng-Shyang ;
Lu, Zhe-Ming .
2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-3, 2006, :406-+
[39]   Direct kernel neighborhood discriminant analysis for face recognition [J].
Hu, Haifeng ;
Zhang, Ping ;
Ma, Zhengming .
PATTERN RECOGNITION LETTERS, 2009, 30 (10) :902-907
[40]   Improving Kernel Fisher Discriminant Analysis for face recognition [J].
Liu, QS ;
Lu, HQ ;
Ma, SD .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2004, 14 (01) :42-49