An efficient and reduced memory indexing approach based on priority rank spectral hashing for multibiometric database

Abstract

Fast retrieval of data from a multibiometric database is a challenging task as the size of the databases has increased considerably. For the retrieval to be faster, the search space has to be narrowed to a smaller set comprising of nearest neighbours. To achieve this, an appropriate data structure is to be built. Hence a novel priority rank-based spectral hashing algorithm is implemented to enhance the efficiency of indexing in a multibiometric database of iris and palmprints. To improve the matching accuracy, GIST feature extraction is used with weighted feature level fusion. From the experimental results, it is concluded that the proposed indexing algorithm has reduced storage cost by 85%, along with reduced penetration rate, false acceptance rate and false rejection rate. In addition, hit rate has improved by 25% compared to the existing kd tree technique.