A New Indexing Method for Biometric Databases Using Match Scores and Decision Level Fusion

Abstract

This paper proposes a new clustering-based indexing technique for large biometric databases. We compute a fixed length index code for each biometric image in the database by computing its similarity against a preselected set of sample images. An efficient clustering algorithm is applied on the database and the representative of each cluster is selected for the sample set. Further, the indices of all individuals are stored in an index table. During retrieval, we calculate the similarity between query image and each of the cluster representative (i.e., query index code) and select the clusters that have similarities to the query image as candidate identities. Further, the candidate identities are also retrieved based on the similarity between index of query image and those of the identities in the index table using voting scheme. Finally, we fuse the candidate identities from clusters as well as index table using decision level fusion. The technique has been tested on benchmark PolyU palm print database consist of 7,752 images and the results show a better performance in terms of response time and search speed compared to the state of art indexing methods.