Fingerprint identification: Classification vs. indexing

In this paper, we present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprint into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm based on Least Square Minimization over each of the possible triplets minutiae pair is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better based on size of search space and identification results.