A Comparison of GEC-Based Feature Selection and Weighting for Multimodal Biometric Recognition

被引：0

作者：

Alford, Aniesha ^{[1
]}

Popplewell, Khary ^{[1
]}

Dozier, Gerry ^{[1
]}

Bryant, Kelvin ^{[1
]}

Kelly, John ^{[1
]}

Adams, Josh ^{[1
]}

Abegaz, Tamirat ^{[1
]}

Shelton, Joseph ^{[1
]}

Ricanek, Karl ^{[2
]}

Woodard, Damon L. ^{[3
]}

机构：

[1] N Carolina Agr & Tech State Univ, Ctr Adv Studies Ident Sci, Greensboro, NC USA

[2] Univ N Carolina Wilmington, Ctr Adv Studies Ident Sci, Wilmington, NC USA

[3] Clemson Univ, Ctr Adv Studies Ident Sci, Clemson, SC 29631 USA

来源：

2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2011年

基金：

美国国家科学基金会;

关键词：

Eigenface; Estimation of Distribution Algorithm; Feature Selection; Feature Weighting; Local Binary Pattern; Steady-State Genetic Algorithm;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we compare the performance of a Steady-State Genetic Algorithm (SSGA) and an Estimation of Distribution Algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeW(SSGA)) produces higher average recognition accuracies, while EDA-based feature selection (GEFeS(EDA)) performs better at reducing the number of features needed for recognition.

引用

页码：2725 / 2728

页数：4

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