Ear Identification Based on KICA and SVM

被引：0

作者：

Wu He-lei ^{[1
]}

Wang Qian ^{[1
]}

Shen Hua-jun ^{[1
]}

Hu Ling-yan ^{[1
]}

机构：

[1] Nanchang Univ, Nanchang 330013, Peoples R China

来源：

PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL IV | 2009年

关键词：

ALGORITHMS;

D O I：

10.1109/GCIS.2009.278

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper gives a research in ear identification After giving an introduction about Independent Components Analysis(ICA), the paper put forward an improved Kernel Independent Components Analysis(KICA) which can be described as a combination of KPCA and ICA to extract features, And use Support Vector Machine(SVM) with Gaussian radial basis Junction(GRBF) for ear classification. The experiments results show the method in the paper gives a high recognition rate compared to ICA method.

引用

页码：414 / 417

页数：4

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