Title | Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network |
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Authors | Jenkin Winston, J., Jude Hemanth, D., Angelopoulou, A. and Kapetanios, E. |
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Type | Conference paper |
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Abstract | The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations. |
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Keywords | Iris |
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| Biometrics |
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| Machine learning |
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| Neural network |
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| Optimization |
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Year | 2019 |
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Conference | The 9th International Conference on Imaging for Crime Detection and Prevention |
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Publisher | Institution of Engineering and Technology (IET) |
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| IEEE |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Publication dates |
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Published | 08 Jul 2020 |
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ISBN | 9781839531095 |
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Digital Object Identifier (DOI) | https://doi.org/10.1049/cp.2019.1171 |
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Web address (URL) of conference proceedings | http://www.icdp-conf.org/ |
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