Speeded Up Robust Features Descriptor for Iris Recognition Systems (JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 2021)
Biometric systems have gained significant attention for several applications. Iris identification was one of the most sophisticated biometrical techniques for effective and confident authentication. Current iris identification system offers accurate and reliable results based on near- infra -red light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without cooperation between the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion and reflection. This works aims to use Speeded up robust features Descriptor (SURF) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. This approach is used and evaluated on the CASIA v1and IITD v1 databases as NIR iris image and UBIRIS v1 as color image. The evaluation results showed a high accuracy rate 98.1 % on CASIA v1, 98.2 on IITD v1 and 83% on UBIRIS v1 evaluated by comparing to the other methods