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

Abstract

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.