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Kernel-based nonlinear discriminant analysis for face recognition
Liu, QS; Huang, R; Lu, HQ; Ma, SD
Source PublicationJOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
2003-11-01
Volume18Issue:6Pages:788-795
SubtypeArticle
AbstractLinear 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.
KeywordLinear Subspace Analysis Kernel-based Nonlinear Discriminant Analysis Kernel-based Principal Component Analysis Face Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordALGORITHMS ; EIGENFACES
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS IDWOS:000187161600013
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9872
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
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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