Applied Graph Theory in Computer Vision and Pattern Recognition

Abstract

Automatic Fingerprint Identification Systems (AFISs) are widely used for criminal investigations for matching the latent fingerprints found at the crime scene with those registered in the police database. As databases usually contain an enormous number of fingerprints, the time required to identify potential suspects can be extremely long. Therefore, a classification phase is performed to whittle down and thus speed up the search. Latent fingerprints are classified into five classes known as Henry classes. In this way each fingerprint only need to be matched against records of the corresponding class contained in the database. Many fingerprint classification methods have been proposed to date, but only a few of these exploit graph-based, or structural, representations of fingerprints. The results reported in the literature indicate that classical statistical methods outperform structural methods for benchmarking fingerprint databases.