الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 174 |  |  |
| | | from | 174 | | |
Abstract
As our everyday life is getting more and more computerized, automated
security systems are getting more and more important. Today most
personal banking tasks can be performed over the Internet and soon they
can also be performed on mobile devices such as cell phones and Personal
Digital Assistant (PDAs).
An accurate automatic personal identification is critical in a wide range of
application domains such as national ID, electronic commerce, and
automated banking. The recent developments in the biometrics area have
lead to smaller, faster and cheaper systems, which in turn has increased the
number of possible application areas for biometric identity verification.
Biometrics, which refers to automatic identification of a person based on
physiological of behavioral characteristics, is inherently more reliable and
more capable in differentiating between an authorized person and a
fraudulent impostor than traditional methods such as passwords and PIN
numbers. Automatic Fingerprint Identification System (AFIS) is one of the
most reliable biometric technologies.
In this thesis, our objective is to find a new method for fingerprint image
representation. The new representation method can be used in fingerprint
image enhancement, classification through the identification system stages
We have identified and explode the following issue: fingerprint images
capturing and structures- define the different types of capturing process
and different types of fingerprint structures that can be used in the
identification system. Feature extraction- finding the representative
features from an input fingerprint image for purpose of fingerprint
matching. Fingerprint matching- determining whether two sets of
fingerprint features are extracted from the same finger. Fingerprint
image enhancement- improving the clarity of ridge structure of fingerprint
images to facilitate automatic extraction of features or for visual inspection.
(v) Fingerprint classification- assigning a fingerprint into one of several prespecified
categories according to its pattern formation.
We developed three new techniques: fingerprint image enhancement
based on frequency domain representation, fingerprint image
enhancement algorithm based on using the new representation method
called wavelet domain, and fingerprint classification based on
frequency domain representation.
Our developed methods have been evaluated extensively on a large
number of fingerprint images captured with the traditional inked method
and more recent inkless optical scanner. Experimental results show that
our proposed algorithms perform very well on the different fingerprint
databases.