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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。
  • 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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  • 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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