Fingerprint indexing based on minutiae pairs and convex core point

Fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms. Fingerprint indexing is a key strategy in automatic fingerprint identification systems (AFISs) which allows us to reduce the number of candidates, the search space, and the occurrences of false acceptance in large databases. In this paper, an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k-means clustering and candidate list reduction criteria to improve the identification performance. Our proposal can effectively reduce the search space and number of candidates for fingerprint matching, and thus achieves higher efficiency and significantly improves the system retrieval performance. Experimental results over some of the fingerprint verification competition (FVC) and the national institute of standards and technology (NIST) databases prove the superiority of the proposed approach against some of the well known indexing algorithms.