Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

被引：77

作者：

Sim, Hiew Moi ^{[1
]}

Asmuni, Hishammuddin ^{[1
]}

Hassan, Rohayanti ^{[2
]}

Othman, Razib M. ^{[3
]}

机构：

[1] Univ Teknol Malaysia, Lab Biometr & Digital Forens, Skudai 81300, Johor, Malaysia

[2] Univ Teknol Malaysia, Lab Biodivers & Bioinformat, Skudai 81300, Johor, Malaysia

[3] Univ Teknol Malaysia, Lab Computat Intelligence & Biotechnol, Skudai 81300, Johor, Malaysia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2014年 / 41卷 / 11期

关键词：

Iris recognition; Face recognition; Weighted score level fusion; Multimodal biometrics; Non-ideal biometrics; RECOGNITION;

D O I：

10.1016/j.eswa.2014.02.051

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：5390 / 5404

页数：15

共 44 条

[21]

Kumar D. R. Shashi, 2011, 2011 Proceedings of International Conference on Computational Intelligence and Communication Networks (CICN 2011), P489, DOI 10.1109/CICN.2011.102

[22]

Mehrotra Hunny., 2006, Proceedings of International Conference on Signal and Image Processing, P1

[23]

MMU, 2004, MMU IR IM DAT

[24]

MODI SK, 2011, ARTECH HSE INF SECUR, P1

[25] Multimodal Biometric System Using Rank-Level Fusion Approach [J].

Monwar, Md. Maruf ;

Gavrilova, Marina L. .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2009, 39 (04) :867-878

[26]

Morizet N, 2008, LECT NOTES COMPUT SC, V5359, P661, DOI 10.1007/978-3-540-89646-3_65

[27]

Parashar R., 2012, INT J ENG RES TECHNO, V1, P2278

[28]

Poh N, 2002, NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, P375, DOI 10.1109/NNSP.2002.1030049

[29]

Popescu-Bodorin N., 2010, P 11 WSEAS INT C AUT, P116

[30] Iris segmentation methodology for non-cooperative recognition [J].

Proenca, H. ;

Alexandre, L. A. .

IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2006, 153 (02) :199-205

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