Abstract
Iris recognition system is one of the most predominant methods used for personal identification in the modern days. Low quality iris image such as low contrast and poor illumination presents a setback for iris recognition as the acceptance or rejection rates of verified user depend solely on the image quality. This paper presents a new method for improving histogram equalization technique to obtained high contrast in normalization process thereby reducing False Rejection Rate (FRR) and False Acceptance Rate (FAR). The proposed technique is developed using C++ and tested using four datasets CASIA, UBIRIS, MMU and ICE 2005. The experimental results show that the proposed technique has an accuracy of 95%, as compared to the existing techniques: CLAHE, AHE, MAHE and HE which have an accuracy of a 93.0, 85.7, 92.8 and 90.71% respectively. Hence it can be concluded that the proposed technique is a better enhancement technique compared to the existing techniques for image enhancement.
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Acknowledgements
This research was fully sponsored by the Office for Research, Innovation, Commercialization and Consultancy (ORICC), with VOT No U614. The authors fully acknowledge Universiti Tun Hussein Onn Malaysia (UTHM) for the financial support which has made this research possible.
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Ghali, A.A., Jamel, S., Mohamad, K.M., Khalid, S.K.A., Pindar, Z.A., Deris, M.M. (2018). An Improved Low Contrast Image in Normalization Process for Iris Recognition System. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_47
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DOI: https://doi.org/10.1007/978-3-319-72550-5_47
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