Morlet wavelet-based feature extraction algorithm with Fuzzy logic for an Iris Recognition System for Non-ideal images

This study investigates the integration of Fuzzy Logic into an Iris Recognition System, with a specific focus on its impact on system performance and its enhancement of the Morlet Wavelet Transform-based feature extraction process. This study explores the integration of Fuzzy Logic into an Iris Recognition System, focusing on enhancing the Morlet Wavelet Transform-based feature extraction process. By addressing non-ideal iris images, including different-sized irises, specular reflections, occlusions, blurred images, and off-angle shots, Fuzzy Logic significantly improves system performance. Despite achieving an overall accuracy of 85.43%, this study acknowledges the potential for further enhancement by optimizing image capture conditions. In comparison to previous research by Panganiban, Linsangan, and Caluyo, which achieved impressive percentages of up to 93% and 94.5% in iris recognition accuracy, albeit in ideal image conditions, there is room for improvement. This study underscores the importance of maintaining consistent lighting and proper iris positioning to minimize errors. With future advancements in algorithms and image capture techniques, even greater accuracy can be achieved. Recommendations include upholding consistent image capture conditions and further exploring the potential of Fuzzy Logic for broader iris recognition applications. The normalization step standardizes the extracted iris region, opening doors for advanced and dependable iris recognition systems in the future.