This paper proposes an efficient retrieval ap- proach for iris using local features. The features are extracted from segmented iris image using scale invariant feature trans- form （SIFT）. The keypoint descriptors ex...This paper proposes an efficient retrieval ap- proach for iris using local features. The features are extracted from segmented iris image using scale invariant feature trans- form （SIFT）. The keypoint descriptors extracted from SIFT are clustered into m groups using k-means. The idea is to perform indexing of keypoints based on descriptor property. During database indexing phase, k-d tree k-dimensional tree is constructed for each cluster center taken from N iris im- ages. Thus for m clusters, rn such k-d trees are generated de- noted as ti, where 1 ~〈 i ~〈 m. During the retrieval phase, the keypoint descriptors from probe iris image are clustered into m groups and ith cluster center is used to traverse correspond- ing ti for searching, k nearest neighbor approach is used, which finds p neighbors from each tree （ti） that falls within certain radius r centered on the probe point in k-dimensional space. Finally, p neighbors from m trees are combined using union operation and top S matches （S c_ （m x p）） correspond- ing to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outper- forms the existing approaches in terms of speed and accuracy.展开更多
文摘This paper proposes an efficient retrieval ap- proach for iris using local features. The features are extracted from segmented iris image using scale invariant feature trans- form （SIFT）. The keypoint descriptors extracted from SIFT are clustered into m groups using k-means. The idea is to perform indexing of keypoints based on descriptor property. During database indexing phase, k-d tree k-dimensional tree is constructed for each cluster center taken from N iris im- ages. Thus for m clusters, rn such k-d trees are generated de- noted as ti, where 1 ~〈 i ~〈 m. During the retrieval phase, the keypoint descriptors from probe iris image are clustered into m groups and ith cluster center is used to traverse correspond- ing ti for searching, k nearest neighbor approach is used, which finds p neighbors from each tree （ti） that falls within certain radius r centered on the probe point in k-dimensional space. Finally, p neighbors from m trees are combined using union operation and top S matches （S c_ （m x p）） correspond- ing to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outper- forms the existing approaches in terms of speed and accuracy.
