Iris recognition based on multi-block Gabor statistical features encoding

Iris recognition has been recently given greater attention in human identification and it's becoming increasingly an active topic in research. This paper presents a personal identification method based on iris. The Method includes three steps. In the first one, the eye image is processed in order to obtain a segmented and normalized eye image by applying an integrodifferential operator, Hough transform and polar transformation. In the second step, the texture of iris image is analyzed by Gabor filters. Then, we have proposed a novel encoding method based on extracting invariant from local region of the iris to create an iris code of 144 bytes. We have studied different statistical descriptors of filtered image. We have calculated the modified Hamming distance between templates to find out the similarity between irises. The method is tested on the Casia v3 database. The experimental results illustrate the effectiveness of this coding in two modes of biometric iris: 100% of rank-one recognition rate and 1.97% of equal error rate in verification. Therefore the coding process is presented to achieve more satisfactory and convincing results than performed by traditional statistical based approaches and low storage requirements as an interesting alternative to Gabor phase coding.