This research focuses on advancing the recognition capabilities of the Hornbill Eye’s Iris through the integration of sophisticated image processing methodologies. Similar to humans, iris patterns of any animals are unique and stable in nature which makes a reliable source for the identity of any individual, this can be particularly valuable in wildlife conservation and management. Animal iris recognition can be employed in the development of monitoring systems for wildlife. This can be crucial for tracking endangered species, monitoring population dynamics, and studying migration patterns. A novel method for the recognition of Iris of the Hornbill is proposed in the paper where collected iris images are processed through Canny Edge Detector for precise edge extraction, followed by Circular Hough Transform for the Localization of the Region of Interest (ROI) that is eye’s iris, and then for the Image Enhancement Un-Sharp Masking is applied to accentuate subtle details and edges; and High Boost Filtering to refines the image by amplifying high-frequency components. The boosted grey Scale Image corresponding to the four LSB’s are used for matching purpose by weighted arithmetic mean of the intensity values. The results obtained are found to give higher accuracy in terms of Recognition rate where Equal error rate, False Acceptance Ratio and False rejection ratio are compared with Human iris Recognition. The experimental results demonstrate the efficiency of the proposed method in achieving robust and reliable recognition outcomes, making it a valuable contribution to the field of biometric animal identification systems.