Local feature based retrieval approach for iris biometrics

This paper proposes an efficient retrieval approach for iris using local features. The features are extracted from segmented iris image using scale invariant feature transform (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 images. Thus for m clusters, m such k-d trees are generated denoted 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 corresponding 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 ⊆ (m× p)) corresponding to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outperforms the existing approaches in terms of speed and accuracy.