For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images.
Distortions are very common in fingerprint images due to elasticity of the skin.
In this paper, a method of indexing large fingerprint image databases is presented. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.