A clustering-based indexing approach for biometric databases using decision-level fusion

Abstract

In this paper, we propose a clustering-based indexing mechanism for biometric databases. The proposed technique relies mainly on a small set of preselected images called representative images. First, the database is partitioned into set of clusters and one image from each cluster is selected for the representative image set. Then, for each image in the database, an index code is computed by comparing it against the representative images. Further, an efficient storage structure (i.e., index space) is developed and the biometric images are arranged in it like traditional database records so that a quick search is possible. During identification, list of candidates which are very similar to the query are retrieved from the index space. Further, to make full use of the clustering, we also retrieve the candidate identities from the selected clusters which are similar to query. Finally, the candidate identities from the index space and cluster space are fused using decision-level fusion. Experimental results on different databases show a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods.