The design and analysis of spatial data structures
The design and analysis of spatial data structures
K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
Finding circles by an array of accumulators
Communications of the ACM
Multidimensional binary search trees used for associative searching
Communications of the ACM
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Use of Artificial Color filtering to improve iris recognition and searching
Pattern Recognition Letters
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Coarse iris classification using box-counting to estimate fractal dimensions
Pattern Recognition
Interactive museum guide: accurate retrieval of object descriptions
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient indexing scheme for iris biometric using k-d-b trees
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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This paper proposes an efficient indexing scheme that can be used for retrieval from a large iris database. For a given color iris query image, the proposed indexing scheme makes use of iris color to determine an index and uses this index to reduce the search space in the large iris database. Further, for query q, the retrieval technique uses iris texture to find the top best match from the reduced search space. The proposed technique has been tested on two publicly available color iris databases, viz UPOL [10] of 384 images and UBIRIS [13] of 1860 fully noisy images and is found to be robust against change in gaze, illumination, partial occlusions and scale. In both the databases, the test reveals that a small subspace is sufficient to achieve 100% hitrate for the top best match under various scales, illumination and partial occlusion. The performance of the proposed indexing scheme is analyzed against the group based color indexing scheme proposed in [14]. The results show that proposed indexing scheme is performing better as compared to group based color indexing scheme with respect to hitrate, penetration rate and CMC curve.