Feature-Level Fusion of Iris and Face for Personal Identification

被引：0
|
作者：
Wang, Zhifang [1 ]
Han, Qi [1 ]
Niu, Xiamu [1 ]
Busch, Christoph [2 ]
机构：
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China
[2] Gjovik Univ Coll, Norwegian Informat Secur Lab, Gjovik, Norway
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 3, PROCEEDINGS | 2009年 / 5553卷
基金：
中国国家自然科学基金;
关键词：
Biometrics; Feature-level; Parallel fusion; Unitary space; CFDA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Feature-level fusion remains a challenging problem for multimodal biometrics. However, existing fusion schemes such as sum rule and weighted sum rule are inefficient in complicated condition. In this paper, we propose an efficient feature-level fusion algorithm for iris and face in parallel. The algorithm first normalizes the original features of iris and face using z-score model, and then take complex FDA as the classifier of Unitary space. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database.). Experimental results show the effectiveness of the proposed algorithm.
引用
收藏
页码：356 / +
页数：3
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