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基于核鑒別共同矢量的小樣本臉像鑒別方法

賀云輝,  趙力,  鄒采榮

賀云輝, 趙力, 鄒采榮. 基于核鑒別共同矢量的小樣本臉像鑒別方法[J]. 電子與信息學(xué)報(bào), 2006, 28(12): 2296-2300.
引用本文: 賀云輝, 趙力, 鄒采榮. 基于核鑒別共同矢量的小樣本臉像鑒別方法[J]. 電子與信息學(xué)報(bào), 2006, 28(12): 2296-2300.
He Yun-hui, Zhao Li, Zou Cai-rong. Face Recognition Based on Kernel Discriminative Common Vectors[J]. Journal of Electronics & Information Technology, 2006, 28(12): 2296-2300.
Citation: He Yun-hui, Zhao Li, Zou Cai-rong. Face Recognition Based on Kernel Discriminative Common Vectors[J]. Journal of Electronics & Information Technology, 2006, 28(12): 2296-2300.

基于核鑒別共同矢量的小樣本臉像鑒別方法

Face Recognition Based on Kernel Discriminative Common Vectors

  • 摘要: 人臉識(shí)別中通常存在小樣本問(wèn)題，使得基于Fisher線性鑒別分析的特征抽取方法存在病態(tài)奇異問(wèn)題。近年來(lái)針對(duì)此問(wèn)題提出了不同的解決方法，其中基于共同鑒別矢量(DCV)的方法成功克服了已有各種方法存在的缺點(diǎn)，有較好的數(shù)值穩(wěn)定性和較低的計(jì)算復(fù)雜度。該文將DCV方法推廣到非線性領(lǐng)域，將兩次Gram-Schmidt正交化過(guò)程，轉(zhuǎn)化為只需計(jì)算兩個(gè)核矩陣和進(jìn)行一次Cholesky分解完成，且得到的非線性Fisher鑒別矢量有標(biāo)準(zhǔn)正交的性質(zhì)。實(shí)驗(yàn)驗(yàn)證了所得KDCV方法的識(shí)別性能優(yōu)于DCV方法。
    Abstract: Face recognition tasks always encounter Small Sample Size (SSS) problem, which leads to the ill-posed problem in Fisher Linear Discriminant Analysis (FLDA). The Discriminative Common Vector (DCV) successfully overcomes this problem for FLDA. In this paper, the DCV is extended to nonlinear case, by performing the Gram-Schmidt orthogonalization twice in feature space, which involving computing two kernel matrices and performing a Cholesky decomposition of a kernel matrix. The experimental results demonstrate that the proposed KDCV achieve better performance than the DCV method.
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  • Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.[2]Yu H, Yang J. A Direct LDA algorithm for high-dimensional data with application to face recognition[J].Pattern Recognition.2001, 34(10):2067-2070[3]Chen L F, Liao H YM, Ko M T, Lin JC, Yu G J. A new LDA-based face recognition system which can solve the small sample size problem[J].Pattern Recognition.2000, 33(10):1713-1726[4]Huang R, Liu Q, Lu H, Ma S. Solving the small size problem of LDA[J].Proc. 16th Intl Conf. Pattern Recognition, Quebec City, Que., Canada.2002, 3(8):29-32[5]Cevikalp H, Neamtu M, Wilkes M, Barkana A. Discriminativecommon vectors for face recognition[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2005, 27(1):4-13[6]Glmezoglu M B, Dzhafarov V, Barkana A. The common vector approach and its relation to principal component analysis[J].IEEE Trans. Speech and Audio Processing.2001, 9(6):655-662[7]Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. England: Cambridge Univ. Press, 2004, Part 2.[8]程云鵬. 矩陣論[M]. 西安: 西北工業(yè)大學(xué)出版社, 2001, 第4章.[9]Foley D H, Sammon J W. An optimal set of discriminant vectors[J].IEEE Trans. on Comput.1975, 24(3):281-289[10]Yang J, Jin Z, Yang JY. Essence of kernel Fisher discriminant: KPCA plus LDA[J].Pattern Recognition.2004, 37(10):2097-2100
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出版歷程
  • 收稿日期:  2005-10-24
  • 修回日期:  2006-04-14
  • 刊出日期:  2006-12-19

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