Fingerprint Classification and Indexing

The identification of a person requires a comparison of her fingerprint with all the fingerprints in a database. This database may be very large (e.g., several million fingerprints) in many forensic and civilian applications. In such cases, the identification typically has an unacceptably long response time. The identification process can be speeded up by reducing the number of comparisons that are required to be performed. Sometimes, information about sex, race, age, and other data related to the individual are available and the portion of the database to be searched can be significantly reduced; however, this information is not always accessible (e.g., criminal identification based on latent fingerprints) and, in the general case, information intrinsic to the biometric samples has to be used for an efficient retrieval. A common strategy to speed up the search is to divide the fingerprint database into a number of bins (based on some predefined classes). A fingerprint to be identified is then required to be compared only to the fingerprints in a single bin of the database based on its class.

Fingerprint classification refers to the problem of assigning a fingerprint to a class in a consistent and reliable way. Although fingerprint matching is usually performed according to local features (e.g., minutiae), fingerprint classification is generally based on global features, such as global ridge structure and singularities.