Admissions Open
Admissions Open
In today’s sensitive environment, for personal authentication, iris recognition is the most attentive technique among the various biometric technologies. In iris recognition systems, when capturing an iris image under unconstrained conditions and without user cooperation, the image quality can be highly degraded by poor focus, off-angle view, motion blur, specular reflection (SR), and other artifacts. The noisy iris images increase the intra-individual variations, thus markedly degrading recognition accuracy. To overcome these problems, we propose a new segmentation technique to handle iris images were captured on less constrained conditions. This technique reduces the error percentage while there are types of noise, such as iris obstructions and specular reflection. The proposed technique starts by determining the expected region of iris using K-means clustering algorithm, then circular Hough transform is used to localize iris boundary. After that, some other technique will be applied to detect and isolate noise regions.