Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System

Authors

  • Defiana Sulaeman Department of Information Technology, Swiss German University, Edutown BSD City, Tangerang 15339
  • Anto Satriyo Nugroho Center for Information and Communication Technology Agency for Assessment & Application of Technology (PTIK-BPPT) Teknologi 3 Bld., 2F, Puspitek Serpong, Tangerang Selatan
  • Maulahikmah Galinium Department of Information Technology, Swiss German University, Edutown BSD City, Tangerang 15339

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2016.10.3.2

Abstract

Perfectly segmenting the area of the iris is one of the most important steps in iris recognition. There are several problematic areas that affect the accuracy of the iris segmentation step, such as eyelids, eyelashes, glasses, pupil (due to less accurate iris segmentation), motion blur, and lighting and specular reflections. To solve these problems, gradient magnitude and Fourier descriptor are employed to do iris segmentation in the proposed Multimodal Biometric Authentication System (MBAS). This approach showed quite promising results, i.e. an accuracy rate of 97%. The result of the iris recognition system was combined with the result of an open-source fingerprint recognition system to develop a multimodal biometrics authentication system. The results of the fusion between iris and fingerprint authentication were 99% accurate. Data from Multimedia Malaysia University (MMUI) and our own prepared database, the SGU-MB-1 dataset, were used to test the accuracy of the proposed system.

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References

Keekre, H.B. & Bharadi, V.A., Ageing Adaptation for Multimodal Biometrics Using Adaptive Feature Set Update Algorithm, Advance Computing Conference, 2009, IACC 2009, IEEE International, IEEE, 2009.

Proenca, H. & Alexandre, L.A., Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures. Pattern Analysis and Machine Intelligence, IEEE Transactions, 29(4) pp. 607-612, 2007.

Sentanoe, S., Nugroho, A.S., Galinium, M., Hartono, R. N., Uliniansyah, M.T. & Layooari, M., Iris Localization using Gradient Magnitude and Fourier Descriptor, in Proceedings of International Conference on Advanced Informatics: Concepts, Theory, and Application, Bandung, Indonesia, 2014.

Daugman, J., Probing the Uniqueness and Randomness of IrisCodes: Results from 200 Iris Pair Comparisons, in Proceedings of the IEEE, 94(11), pp. 1927-1935, 2006.

Ross, A. & Shah, S., Segmenting Non-Ideal Irises Using Geodesic Active Contours, Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, IEEE Baltimore, Maryland, USA, 2006.

Abhyankar, A. & Schuckers, S., Active Shape Models for Effective Iris Segmentation, Proceedings of SPIE The International Society for Optical Engineering, Defense and Security Symposium, Florida, USA, 2006.

Valentina, C., Hartono, R.N., Tjahja, T.V. & Nugroho, A.S., Iris Localization using Circular Hough Transform and Horizontal Projection Folding, in Proceedings of International Conference of information Technology and Applied Mathematics Jakarta, Indonesia, pp. 64-68, 2012.

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Published

2016-10-31

How to Cite

Sulaeman, D., Nugroho, A. S., & Galinium, M. (2016). Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System. Journal of ICT Research and Applications, 10(3), 209-227. https://doi.org/10.5614/itbj.ict.res.appl.2016.10.3.2

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Articles