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首页> 外文会议>Iberian Conference on Pattern Recognition and Image Analysis(IbPRIA 2005) pt.2; 20050607-09; Estoril(PT) >A Novel One-Parameter Regularized Kernel Fisher Discriminant Method for Face Recognition
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A Novel One-Parameter Regularized Kernel Fisher Discriminant Method for Face Recognition

机译：一种新颖的单参数正则化核Fisher判别人脸识别方法

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摘要

Kernel-based regularization discriminant analysis (KRDA) is one of the promising approaches for solving small sample size problem in face recognition. This paper addresses the problem in regularization parameter reduction in KRDA. From computational complexity point of view, our goal is to develop a KRDA algorithm with minimum number of parameters, in which regularization process can be fully controlled. Along this line, we have developed a Kernel 1-parameter RDA (K1PRDA) algorithm (W. S. Chen, P C Yuen, J Huang and D. Q. Dai, "Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition," IEEE Transactions on SMC-B, to appear, 2005.). K1PRDA was developed based on a three-parameter regularization formula. In this paper, we propose another approach to formulate the one-parameter KRDA (1PRKFD) based on a two-parameter formula. Yale B database, with pose and illumination variations, is used to compare the performance of 1PRKFD algorithm, K1PRDA algorithm and other LDA-based algorithms. Experimental results show that both 1PRKFD and K1PRDA algorithms outperform the other LDA-based face recognition algorithms. The performance between 1PRKFD and K1PRDA algorithms are comparable. This concludes that our methodology in deriving the one-parameter KRDA is stable.
机译：基于核的正则化判别分析（KRDA）是解决人脸识别中小样本量问题的有前途的方法之一。本文解决了KRDA中正则化参数约简的问题。从计算复杂度的角度来看，我们的目标是开发一种参数数量最少的KRDA算法，在其中可以完全控制正则化过程。沿着这一思路，我们开发了一种内核1参数RDA（K1PRDA）算法（WS Chen，PC Yuen，J Huang和DQ Dai，“基于内核机器的单参数正则化Fisher判别人脸识别方法”，IEEE Transactions SMC-B，将于2005年推出。）。 K1PRDA是根据三参数正则公式开发的。在本文中，我们提出了另一种基于两参数公式来公式化一参数KRDA（1PRKFD）的方法。 Yale B数据库具有姿势和光照变化，用于比较1PRKFD算法，K1PRDA算法和其他基于LDA的算法的性能。实验结果表明，1PRKFD和K1PRDA算法均优于其他基于LDA的面部识别算法。 1PRKFD和K1PRDA算法之间的性能相当。由此得出结论，我们推导一参数KRDA的方法是稳定的。

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