Abstract
Recently, more interest was observed in iris-based biometrics identity recognition. This growth can be observable due to high identity recognition accuracy guaranteed by this measurable trait. In the literature, one can easily find diversified approaches and algorithms connected with this feature, however, neither of them uses discrete fast Fourier transform to describe iris sample. In this work, the authors used recently mentioned method to create feature vector and to verify human identity with diversified classifiers, e.g., artificial neural network. Before these steps, iris image was preprocessed with precisely selected operations. During the research, the authors considered different ways of image preprocessing as well as diversified ideas regarding highlighting of the most important parts of iris. Selected elements can have huge influence on a feature vector and recognition rate. Specialized framework for algorithm testing was proposed. Tests have shown that satisfactory results can be obtained with iris-based human identity recognition with feature vector consisting of the most descriptive components of discrete fast Fourier transform.
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Acknowledgements
This work was partially supported by grant W/WI-IIT/2/2019 and subvention for scientific work for Institute of Technical Informatics and Telecommunications WZ/WI-IIT/4/2020 from Białystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.
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Szymkowski, M., Jasiński, P., Saeed, K. (2022). Iris-Based Approach to Human Identity Recognition by Discrete Fast Fourier Transform Components. In: Chaki, R., Chaki, N., Cortesi, A., Saeed, K. (eds) Advanced Computing and Systems for Security: Volume 13. Lecture Notes in Networks and Systems, vol 241. Springer, Singapore. https://doi.org/10.1007/978-981-16-4287-6_6
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