Adaptive Approach for Detection and Localization of Iris Features for Authentication using Digital Image Processing Techniques


  • Ashwini Baburao Chate N.H College of Engineering, Parli-Vaijnath, Maharashtra, India.
  • Sushilkumar N. Holambe TPCT'S College of Engineering Dharashiv, Maharashtra, India., DEAN R&D


Iris Features, Digital Image Processing, Authentication


Person identification based on iris recognition has gained substantial attention in recent years due to its high accuracy and non-intrusive nature. This study focuses on the detection and localization of key iris features essential for precise person identification. Digital image processing techniques, including preprocessing, segmentation, feature extraction, and classification, are employed to enhance the accuracy and efficiency of iris recognition systems. The preprocessing stage involves noise reduction, normalization, and enhancement to prepare the iris image for subsequent analysis. Segmentation isolates the iris region from the overall eye image, enabling precise feature extraction. Essential iris features, such as the pupil, iris boundary, and unique texture patterns, are then extracted to construct an accurate representation of the individual's iris. To achieve reliable person identification, a robust classification algorithm is utilized to match the extracted iris features with a pre-existing database. Machine learning and pattern recognition techniques play a pivotal role in accurately identifying individuals based on their iris features. The proposed system demonstrates promising results in terms of accuracy and efficiency, making it suitable for various applications, including security systems and access control. Furthermore, the study discusses potential advancements and future prospects in iris recognition technology, aiming to improve the overall performance and applicability of person identification systems. These advancements encompass novel algorithms, hardware enhancements, and integration with emerging technologies, paving the way for more reliable and secure person identification in diverse real-world scenarios.


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Scotti, Fabio, and Vincenzo Piuri. "Adaptive reflection detection and location in iris biometric images by using computational intelligence techniques." IEEE transactions on instrumentation and measurement 59.7 (2009): 1825-1833.

Nabti, Makram, and Ahmed Bouridane. "An effective and fast iris recognition system based on a combined multiscale feature extraction technique." Pattern recognition 41.3 (2008): 868-879.

Alheeti, Khattab M. Ali. "Biometric iris recognition based on hybrid technique." International Journal on Soft Computing 2.4 (2011): 1.

Jan, Farmanullah, Saleh Alrashed, and Nasro Min-Allah. "Iris segmentation for non-ideal Iris biometric systems." Multimedia Tools and Applications (2021): 1-29.

Ma, Li, et al. "Efficient iris recognition by characterizing key local variations." IEEE Transactions on Image processing 13.6 (2004): 739-750.

Shah, Samir, and Arun Ross. "Iris segmentation using geodesic active contours." IEEE Transactions on Information Forensics and Security 4.4 (2009): 824-836.

Joshi, Khushbu, and Manish I. Patel. "Recent advances in local feature detector and descriptor: a literature survey." International Journal of Multimedia Information Retrieval 9.4 (2020): 231-247.

Gupta, Rachit Kumar, Mandeep Kaur, and Jatinder Manhas. "Tissue level based deep learning framework for early detection of dysplasia in oral squamous epithelium." Journal of Multimedia Information System 6.2 (2019): 81-86.

Shepal, Yogesh R., and Ashraf Shaikh. "A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data." International Journal of Research Studies in Science 1.7 (2014): 1-6.

Hamd, Muthana H., and Samah K. Ahmed. "Biometric system design for iris recognition using intelligent algorithms." International Journal of Modern Education and Computer Science 10.3 (2018): 9-16.

Alheeti, Khattab M. Ali. "Biometric iris recognition based on hybrid technique." International Journal on Soft Computing 2.4 (2011)

Dhabliya, D., Ugli, I.S.M., Murali, M.J., Abbas, A.H.R., Gulbahor, U. Computer Vision: Advances in Image and Video Analysis (2023) E3S Web of Conferences, 399, art. no. 04045, .

Gandhi, L. ., Rishi, R. ., & Sharma, S. . (2023). An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 01–10.

Pekka Koskinen, Pieter van der Meer, Michael Steiner, Thomas Keller, Marco Bianchi. Automated Feedback Systems for Programming Assignments using Machine Learning. Kuwait Journal of Machine Learning, 2(2). Retrieved from




How to Cite

Chate, A. B. ., & Holambe, S. N. . (2023). Adaptive Approach for Detection and Localization of Iris Features for Authentication using Digital Image Processing Techniques . International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 82–87. Retrieved from



Research Article