Quality Fusion Based Multimodal Eye Recognition

被引：0

作者：

Zhou, Zhi ^{[1
]}

Du, Eliza Yingzi ^{[1
]}

Belcher, Craig ^{[1
]}

Thomas, N. Luke ^{[1
]}

Delp, Edward J. ^{[2
]}

机构：

[1] Indiana Univ Purdue Univ, Biometr & Pattern Recognit Lab, Elect & Comp Engn Dept, Indianapolis, IN 46202 USA

[2] Purdue Univ, Elect & Comp Engn Dept, W Lafayette, IN 47907 USA

来源：

PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2012年

关键词：

Eye recognition; Quality measure; Iris recognition; Sclera recognition; Quality fusion;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Multimodal eye recognition can improve the biometric systems recognition accuracy by combining iris and sclera recognition. However, poor quality images can significantly affect the system performance. In this paper, we proposed a quality fusion based multimodal eye recognition. Our quality measure evaluated the entire eye image quality, iris area quality, and sclera area quality. The experimental results show that our overall iris and sclera quality scores are highly correlated to recognition accuracy, and our quality fusion based eye recognition can improve and predict the performance of eye recognition systems.

引用

页码：1297 / 1302

页数：6

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