Abstract
Iris recognition has been recently given greater attention in human identification and it is becoming increasingly an active topic in research. This paper presents a novel iris recognition method based on multi-channel Gabor filtering and uniform local binary patterns (ULBP). First, the eye image is processed in order to obtain a segmented and normalized eye image by applying Hough transform and polar transformation. Second, the iris image is analyzed by Gabor filters to extract the global features of texture details. Then, ULBP operators are applied in each transformed image to describe the local arrangement of iris texture patterns. Next, the obtained representation is partitioned in blocks. Finally, we have encoded the local relationships between statistical measures computed in blocks to form a template of 240 bytes. We estimate the similarity between irises by computing the modified Hamming distance between templates. Tests were carried out on CASIA v3 iris database. Experimental results illustrate the effectiveness and robustness of ULBP to extract rich local and global information of iris texture when combined with simultaneously multi-blocks and multi-channel method. The comparative evaluations illustrate the good discriminative properties of extracted features for iris recognition.
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