Hash-based space partitioning approach to iris biometric data indexing

Abstract

In spite of high efficiency of the iris recognition systems, the accuracy of the system degrades with the increase in the size of database, hence there is need for indexing. One of the important characteristics of such an indexing approach is to have high tolerance against feature deviation due to noise. In this work, an indexing approach has been proposed which deals with the feature deviation due to variation in quality of iris images. Further, an efficient index space has been developed using two set of hash functions. During identification, a set of probable bin location for the queried data is decided depending upon the level of noise in the query image. Such a mechanism will help in carrying out efficient searching of the queried data. Next, the list of candidate data set which are very similar to the query data are retrieved from the probable bin locations. Considering retrieval of iris template up to the fifth rank, the proposed approach gives high hit rate even at low penetration rate when tested with, IITD, CASIA-V3 Interval and UBIRIS 2 iris databases compared to the existing approaches.