Comparative study of features for fingerprint indexing

For current fingerprint indexing schemes, global textures and minutiae structures are usually utilized. To extend the existing methods of feature extraction, we study the three most popular local descriptors, SIFT, SURF and DAISY, for fingerprint indexing and give a comparison of indexing performance for evaluation of these three features on public fingerprint databases. For index construction, the locality-sensitive hashing (LSH) is used to efficiently retrieve similarity queries in a small fraction of the database. Experiments show that SURF and DAISY are applicable for fingerprint indexing as SURF features perform equally well or better than SIFT features while DAISY improves not so significantly.