I. 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].
Two approaches will be described in this project for fingerprint recognition:
• Approach 1: Based on minutiae located in a fingerprint
• Approach 2: Based on frequency content and ridge orientation of a fingerprint
II. 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.



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