08-05-2014, 04:42 PM
Adaptive Filtering and Neural Networks
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Introduction
Fingerprints are imprints formed by friction ridges of the skin and thumbs. They
have long been used for identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character of the pattern on each
finger. Individuality refers to the uniqueness of ridge details across individuals; the
probability that two fingerprints are alike is about 1 in 1.9x1015.
However, manual fingerprint verification is so tedious, time consuming and expensive that
is incapable of meeting today’s increasing performance requirements. An automatic
fingerprint identification system is widely adopted in many applications such as building or
area security and ATM machines [1-2].
First Approach
Most automatic systems for fingerprint comparison are based on minutiae matching
Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different
minutiae types have been identified. In practice only ridge ending and ridge bifurcation
minutiae types are used in fingerprint recognition. Examples of minutiae are shown in
figure 1.
Edge Detection
An edge is the boundary between two regions with relatively distinct gray level
properties. The idea underlying most edge-detection techniques is on the computation of a
local derivative operator such as ‘Roberts’, ‘Prewitt’ or ‘Sobel’ operators.
In practice, the set of pixels obtained from the edge detection algorithm seldom
characterizes a boundary completely because of noise, breaks in the boundary and other
effects that introduce spurious intensity discontinuities. Thus, edge detection algorithms
typically are followed by linking and other boundary detection procedures designed to
assemble edge pixels into meaningful boundaries.
For a detailed explanation refer to “Digital Image Processing” by Gonzalez, chapters 3 - 4.
It is also useful to check the Image Toolbox Demos available in MATLAB