Multimodal Biometric Person Recognition by Feature Fusion

被引：0

作者：

Huang, Lin ^{[1
]}

Yu, Chenxi ^{[2
]}

Cao, Xinzhe ^{[3
]}

机构：

[1] Metro State Univ Denver, Dept Engn & Engn Technol, Denver, CO 80204 USA

[2] Dianguang Equipment Inst, Dept Technol, Luoyang, Peoples R China

[3] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA

来源：

2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018) | 2018年

关键词：

biometrics; multimodal biometrics; bimodal biometrics; feature extraction; fusion; Simulated Annealing; Genetic Algorithm; ALGORITHM;

D O I：

10.1109/ICISCE.2018.00238

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fusion for multimodal biometrics can be done in various levels. Among them, the score and decision fusion levels have been widely studied in the literature, but feature fusion level is a relatively understudied problem. This paper proposes a feature fusion method based on the Simulated Annealing (SA) technique. A study is conducted to compare the results obtained by the SA technique with the ones obtained by the Genetic Algorithm (GA). The experimental result shows that both methods achieve the same accuracy performance in feature fusion level, while the SA technique is computationally more efficient than the GA method.

引用

页码：1158 / 1162

页数：5

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