Postmatch pruning of SIFT pairs for iris recognition

Abstract

This article looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like scale invariant feature transform SIFT gives satisfactory recognition accuracy for good quality images. However the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The paper proposes two methods of filtering impairments faulty pairs based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms angular filtering and scale filtering are applied separately and applied in union to have a complete comparative analysis of the result of matching. The pruning approaches has given better recognition accuracy than conventional SIFT when experimented on two publicly available BATH and CASIAv3 iris databases.