Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

The main reason for the popularity of automatic fingerprint
identification is to speed up the matching (searching) process. Manual
matching of fingerprints is a highly tedious task because the matching
complexity is a function of the size of the image database which can vary
from a few hundred records to several million records, which takes several
days in some cases. The manual classification method makes the distribution
of records uneven resulting in more work for commonly occurring fingerprint
classes. These problems can be overcome by automating the fingerprint based
identification process.
Speed of an automated fingerprint identification can be increased
drastically by grouping the images into different classes depending upon their
features, so that searching can be done only with images of that class, instead
of all the images thus reducing the search space. So, whenever an image is
submitted for identification, the following processes are to be carried out:
i) identification of the class, to which it belongs
ii) comparison of the sample fingerprint with the existing fingerprint images
of that class.
23.3.1 Features Used for Classification
A ridge is defined as a line on the fingerprint. A valley, on the other hand, is
defined as a low region, more or less enclosed by hills of ridges. Each
fingerprint is a map of ridges and valleys in the epidermis layer of the skin.
Ridge and valley structure form unique geometric patterns. In a fingerprint,
the ridges and valley alternate flowing in a local constant direction. A closer
analysis of the fingerprint reveals that the ridges (or valleys) exhibit anomalies
of various kinds such as ridge bifurcation, ridge endings, short ridges and
ridge cross over [15]. These features are collectively called minutiae and
these minutiae have a pattern that is unique for each fingerprint. The
directions of the ridges, the relative positions of the minutiae and the number
of ridges between any pair of minutiae are some of the features that uniquely
characterize a fingerprint. Automated fingerprint identification and
verification systems that use these features are considered minutiae based.
Vast majorities of contemporary automated fingerprint identification and
verification systems are minutiae based systems.
In this work, we however considered singular points to classify the
fingerprints. Two distinct types of singular points have been used to identify
fingerprints. These are core and delta points. Fig. 23.4, presented below,
describes these singular points.