An efficient ANN Based approach for Latent Fingerprint Matching

Abstract

Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, while others look for similarities in the bigger structure of the fingerprint. A major approach for fingerprint recognition today is to extract minutiae from fingerprint images and to perform fingerprint matching based on the number of corresponding minutiae pairings. One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. In this paper we propose a method for offline fingerprint matching based on minutiae matching. However, unlike conventional minutiae matching algorithms, our algorithm also takes into account region and line structures that exist between minutiae pairs. This allows filling the small breaks in the curves created because of uneven surface and uneven pressure.

An Efficient Automatic Overlapped Fingerprint Identification and Recognition Using ANFIS Classifier

“…Most of the existing fingerprint-matching algorithms [1,[3][4][5][8][9][10][11][12][13], and [14] failed to match the overlapped images in an efficient manner. VeriFinger and FingerTec algorithms are unable to split the two fingerprints effectively, and there is no successful extraction of the ridges in the overlapped area.…”