  This paper proposes an efficient three fold stratified SIFT matching for iris
recognition. The objective is to filter wrongly paired conventional SIFT
matches. In Strata I, the keypoints from gallery and probe iris images are
paired using traditional SIFT approach. Due to high image similarity at
different regions of iris there may be some impairments. These are detected and
filtered by finding gradient of paired keypoints in Strata II. Further, the
scaling factor of paired keypoints is used to remove impairments in Strata III.
The pairs retained after Strata III are likely to be potential matches for iris
recognition. The proposed system performs with an accuracy of 96.08% and 97.15%
on publicly available CASIAV3 and BATH databases respectively. This marks
significant improvement of accuracy and FAR over the existing SIFT matching for
iris.
