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Efficient Iris Recognition Management in Object-Related Databases
DOI:
https://doi.org/10.24215/16666038.18.e12Keywords:
IrisCode, index, database, object relational, extensionAbstract
Biometric applications have grown significantly in recent years, particularly iris-based systems. In the present work, an extension of an Object Relational Database Management System for the integral management of a biometric system based on the human iris was presented. Although at present, there are many database extensions for different domains, in no case for biometric applications. The proposed extension includes both the extension of the type system and the definition of domain indexes for performance improvement. The aim of this work is to provide a tool that facilitates the development of biometric applications based on the iris feature. Its development is based on a reference architecture that includes both the management of images of the iris trait, its associated metadata and the necessary methods for both manipulation and queries. An implementation of the extension is performed for PostgreSQL DBMS, and SP-GiST framework is used in the implementation of a domain index. Experiments were carried out to evaluate the performance of the proposed index, which shows improvements in query execution times.
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[8] N. Poonguzhali and M. Ezhilarasan, Iris Indexing Techniques: A Review. International Journal of Computer Applications. no. 73. pp 23-29, 2013.
[9] R. Mukherjee, Indexing Techniques for Fingerprint and Iris Databases. Master thesis, College of Engineering and Mineral Resources at West Virginia University, 2007.
[10] J. Daugman, High con dence visual recognition of people by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 15(11), pp. 1148-1161, 1993.
[11] J. Daugman and C. Downing, Effect of severe image compression on iris recognition performance, IEEE Transactions on Information Forensics and Security, vol. 3, no. 1, p. 52–61, 2008.
[12]A. W. K. Kong, D. Zhang and M. S. Kamel, An Analysis of IrisCode, in IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 522-532, 2010.
[13]P. Clements, F. Bachmann, L. Bass, D. Garlan, J. Ivers, R. Little, P. Merson, R. Nord and J. Stafford, Documenting Software Architectures: Views and Beyond. 2nd Edition. Addison-Wesley Professional. 2010.
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[28]W. Burkhard and R. Keller, Some approaches to best-match file searching. Communications of the ACM. ACM New York, NY, USA, vol. 16(4), pp. 230–236, 1973.
[29]W. Aref and I. Ilyas, SP-GiST: An Extensible Database Index for Supporting Space Partitioning Trees, Journal of Intelligent Information Systems (JIIS), (2/3), pp. 215 -240, 2001.
[30]“CASIA Iris Database, CASIA-IrisV4-Thousand”. Available at: http://http://biometrics.idealtest.org/#. Accessed on 2018-01-03.
Published
2018-10-04
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How to Cite
[1]
“Efficient Iris Recognition Management in Object-Related Databases”, JCS&T, vol. 18, no. 02, p. e12, Oct. 2018, doi: 10.24215/16666038.18.e12.
