Abstract
Numerous researchers have proposed iris recognition systems that use various techniques for extracting features for accurate and dependable biometric authentication. This study proposes and implements a statistical feature extraction approach based on the correlation between neighboring pixels. Image processing and enhancement methods are utilized to identify the iris. Statistical characteristics have been used to assess a system's performance. Experiments on the influence of a wide range of statistical characteristics have also been carried out. The studies’ findings, which were based on a unique collection of statistical properties of iris scans, reveal a considerable improvement. A receiver operating characteristic curve is used to do the performance analysis.
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Gaikwad, S.S., Gaikwad, J.S. (2023). A Novel Approach for Iris Recognition Model Using Statistical Feature Techniques. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Shahnaz, C. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_47
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DOI: https://doi.org/10.1007/978-981-19-4052-1_47
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