We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
LIU QingShan, HUANG Rui, LU HanQing, MA SongDe. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition[J]. Journal of Computer Science and Technology, 2003, 18(6).
Citation: LIU QingShan, HUANG Rui, LU HanQing, MA SongDe. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition[J]. Journal of Computer Science and Technology, 2003, 18(6).

Kernel-Based Nonlinear Discriminant Analysis for Face Recognition

More Information
  • Published Date: November 14, 2003
  • Linear subspace analysis methods havebeen successfully applied to extract features for face recognition. Butthey are inadequate to represent the complex and nonlinear variations ofreal face images, such as illumination, facial expression and posevariations, because of their linear properties. In this paper, anonlinear subspace analysis method, Kernel-based Nonlinear DiscriminantAnalysis (KNDA), is presented for face recognition, which combines thenonlinear kernel trick with the linear subspace analysis method --- FisherLinear Discriminant Analysis (FLDA). First, the kernel trick is used toproject the input data into an implicit feature space, then FLDA isperformed in this feature space. Thus nonlinear discriminant featuresof the input data are yielded. In addition, in order to reduce thecomputational complexity, a geometry-based feature vectors selectionscheme is adopted. Another similar nonlinear subspace analysis isKernel-based Principal Component Analysis (KPCA), which combines thekernel trick with linear Principal Component Analysis (PCA). Experimentsare performed with the polynomial kernel, and KNDA is compared with KPCAand FLDA. Extensive experimental results show that KNDA can give ahigher recognition rate than KPCA and FLDA.
  • Related Articles

    [1]Mathu Soothana S. Kumar Retna Swami, Muneeswaran Karuppiah. Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition[J]. Journal of Computer Science and Technology, 2013, 28(2): 322-328. DOI: 10.1007/s11390-013-1333-5
    [2]Feng-Xi Song, David Zhang, Cai-Kou Chen, Jing-Yu Yang. Facial Feature Extraction Method Based on Coefficients of Variances[J]. Journal of Computer Science and Technology, 2007, 22(4): 626-632.
    [3]Ying-Han Pang, Andrew T. B. J., David N. C. L. Two-Factor Cancelable Biometrics Authenticator[J]. Journal of Computer Science and Technology, 2007, 22(1): 54-59.
    [4]Xin Geng, Zhi-Hua Zhou. Image Region Selection and Ensemble for Face Recognition[J]. Journal of Computer Science and Technology, 2006, 21(1): 116-125.
    [5]Pin Liao, Li Shen, Yi-Qiang Chen, Shu-Chang Liu. Unified Model in Identity Subspace for Face Recognition[J]. Journal of Computer Science and Technology, 2004, 19(5).
    [6]Pin Liao, Li Shen. Unified Probabilistic Models for Face Recognition from a SingleExample Image per Person[J]. Journal of Computer Science and Technology, 2004, 19(3).
    [7]WU Xiaojun, YANG Jingyu, WANG Shitong, GUO Yuefei, CAO Qiying. A New Algorithm for Generalized Optimal Discriminant Vectors[J]. Journal of Computer Science and Technology, 2002, 17(3).
    [8]HUANG YU, XU Guangyou, ZHU Yuanxin. Extraction of Spatial-Temporal Features for Vision-Based Gesture Recognition[J]. Journal of Computer Science and Technology, 2000, 15(1): 64-72.
    [9]Zhang Yongyue, Peng Zhenyun, You Suya, Xu Guangyou. A Multi-View Face Recognition System[J]. Journal of Computer Science and Technology, 1997, 12(5): 400-407.
    [10]Zhang Xinzhong, Yan Changde, Liu Xiuying. Feature Point Method of Chinese Character Recognition and Its Application[J]. Journal of Computer Science and Technology, 1990, 5(4): 305-311.

Catalog

    Article views (19) PDF downloads (1837) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return