Fingerprint classification by directional fields

Abstract

Fingerprint classification provides an important fingerprint index and can reduce fingerprint matching time in a large database. A good classification algorithm can give an accurate index that is able to search a fingerprint database more effectively. We present a fingerprint classification algorithm that is based on directional fields. We compute directional fields of fingerprint images and detect singular points (cores). Then, we extract features that we define from fingerprint images. We also use k-means classifier and 3-nearest neighbor to classify features and distinguish which fingerprint is Arch, Left Loop, Right Loop, or Whorl. Experimental results show a significant improvement in fingerprint classification performance. Moreover, the time required for the classification algorithm is reduced.