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Kernel-based nonlinear discriminant analysis for face recognition
Liu, QS; Huang, R; Lu, HQ; Ma, SD
2003-11-01
发表期刊JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
卷号18期号:6页码:788-795
文章类型Article
摘要Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA). First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.
关键词Linear Subspace Analysis Kernel-based Nonlinear Discriminant Analysis Kernel-based Principal Component Analysis Face Recognition
WOS标题词Science & Technology ; Technology
关键词[WOS]ALGORITHMS ; EIGENFACES
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号WOS:000187161600013
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9872
专题09年以前成果
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
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GB/T 7714
Liu, QS,Huang, R,Lu, HQ,et al. Kernel-based nonlinear discriminant analysis for face recognition[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2003,18(6):788-795.
APA Liu, QS,Huang, R,Lu, HQ,&Ma, SD.(2003).Kernel-based nonlinear discriminant analysis for face recognition.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,18(6),788-795.
MLA Liu, QS,et al."Kernel-based nonlinear discriminant analysis for face recognition".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 18.6(2003):788-795.
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