Iris Image Classification Using SIFT Features

The object of interest of this paper is automatic iris classification when dealing with missing information. Our approach uses and extends a method for face recognition, based on Scale Invariant Feature Transform (SIFT). We adapted this method for iris classification and tested it on occluded iris images. We add to the keypoint matching procedure new conditions that improve the classification rate. We tested different parameters involved in the SIFT extraction process and the keypoint matching scheme on eleven image datasets with different levels of occlusion. For testing, a standardized segmented UPOL iris database was employed. We experimentally prove that the proposed approach has better results when compared with both the original method and the Daugman procedure on all datasets. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.