Fingerprint classification provides an important fingerprint index and can reduce fingerprint matching time in large database. A good classification algorithm can give an accurate index that is able to search a fingerprint database more effectively.

In this paper, we present a fingerprint classification algorithm that is based on directional fields. We compute directional fields of fingerprint image and detect singular points (cores). Then, we extract features that we define from fingerprint image. We also use k-means classifier and 3-nearest neighbor to classify feature 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.