Enhancing iris recognition system performance using templates fusion

被引:10
|
作者
Desoky, Aly I. [1 ]
Ali, Hesham A. [1 ]
Abdel-Hamid, Nahla B. [1 ]
机构
[1] Mansoura Univ, Fac Eng, Dept Comp Sci, Mansoura, Egypt
关键词
Iris recognition; Image fusion; Template matching; Performance management; Biometric systems;
D O I
10.1016/j.asej.2011.06.003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Iris recognition has very high recognition accuracy in comparison with many other biometric features. This paper proposes an iris recognition algorithm in which a set of iris images of a given eye are fused to generate a final template using the most consistent feature data. Features consistency weight matrix is determined according to the noise level presented in the considered images. A new metric measure formula using Hamming distance is proposed. Such an algorithm has the capability of reducing the amount of data storage and accelerate the matching process. Simulation studies are made to test the validity of the proposed algorithm. The results obtained ensure the superior performance of such algorithm over any other one. (C) 2011 Ain Shams University. Production and hosting by Elsevier B.V. All rights reserved.
引用
收藏
页码:133 / 140
页数:8
相关论文
共 50 条
  • [1] Enhancing Iris recognition system performance
    Desoky, Aly I.
    Ali, Hesham A.
    Abdel-Hamid, Nahla B.
    [J]. ICCES'2010: THE 2010 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING & SYSTEMS, 2010, : 21 - 26
  • [2] Iris recognition using templates fusion with weighted majority voting
    Gupta, Rashmi
    Gupta, Kirti
    [J]. INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2016, 7 (04) : 325 - 338
  • [3] Effects of Enrollment Templates Count on Iris Recognition Performance using Reliable Bits
    Ziauddin, Sheikh
    Kalsoom, Sajida
    [J]. 2013 10TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 2013, : 25 - 30
  • [4] Evolution of Performance Analysis of Iris Recognition System By using Hybrid Methods of feature Extraction and Matching by Hybrid Classifier for Iris Recognition System
    Gale, Aparna G.
    Salankar, Suresh S.
    [J]. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 3259 - 3263
  • [5] On Fusion for Multispectral Iris Recognition
    Wild, Peter
    Radu, Petru
    Ferryman, James
    [J]. 2015 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB), 2015, : 31 - 37
  • [6] Enhancing iris recognition framework using feature selection and BPNN
    A. Alice Nithya
    C. Lakshmi
    [J]. Cluster Computing, 2019, 22 : 12363 - 12372
  • [7] Enhancing iris recognition framework using feature selection and BPNN
    Nithya, A. Alice
    Lakshmi, C.
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 5): : 12363 - 12372
  • [8] Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing
    Vatsa, Mayank
    Singh, Richa
    Noore, Afzel
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2008, 38 (04): : 1021 - 1035
  • [9] Iris recognition based on score level fusion by using SVM
    Park, Hyun-Ae
    Park, Kang Ryoung
    [J]. PATTERN RECOGNITION LETTERS, 2007, 28 (15) : 2019 - 2028
  • [10] Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion
    Chen, Ying
    Liu, Yuanning
    Zhu, Xiaodong
    Chen, Huiling
    He, Fei
    Pang, Yutong
    [J]. SCIENTIFIC WORLD JOURNAL, 2014,