一种用于特征提取和人脸识别的广义核Fisher判别框架

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2016-08-01 DOI:10.1109/FSKD.2016.7603396

Yuexiang Shi, Xiaoxue Ren, Saizhou Yang, Ping Gong

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引用次数: 5

摘要

本文将改进的核费雪判别法(KFD)应用于人脸识别。为了最大限度地利用“双判别子空间”中的两类判别信息，提出了一种广义核Fisher判别分析方法。它还可以统一DSDA的两个子空间中的判别函数。在ORL人脸数据库上的实验结果表明了该方法的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A generalized Kernel Fisher Discriminant framework used for feature extraction and face recognition

In this paper, an improved Kernel Fisher Discriminant (KFD) method is used in face recognition. A Generalized Kernel Fisher Discriminant Analysis (GKFD) is proposed to make the most of two kinds of discriminant information in “double discriminant subspaces”. It can also uniform the discriminant functions in two subspaces of DSDA. Experimental results on ORL face database show the feasibility of the suggested method.

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2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

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