Parallel geometric hashing for robust iris indexing
In this paper an effort has been made to improve the time complexity of existing geometric hashing based indexing approach for iris biometrics [1]. In the conventional approach, the annular iris image is used for the extraction of keypoints using Scale Invariant Feature Transform [2]. Further, geometric hashing [3] is used to index the database using extracted keypoints. The existing approach performs with an accuracy of 98.5% with improvement in time. However, to further improve time complexity, existing geometric hashing approach is made parallel during indexing as well as retrieval phase. In the proposed approach, the extracted keypoints are mapped to the processors of the hypercube through shared global memory. The geometric invariants are obtained for each basis pair allotted to individual processors in parallel. During indexing phase, these invariants are stored in the hash table. For iris retrieval, the invariants are obtained and the corresponding entries in the hash table receive a vote. The time complexity of the proposed approach is O(Mn 2) for M iris images each having n keypoints, in comparison to existing approach with time complexity of O(Mn 3). This marks the suitability of proposed approach for real-time applications.
geometric hashing indexing hypercube sift simd