الفهرس 
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Abstract
Support Vector Machines (SVMs) are important to the pattern recognition
problem, relying on a fairly solid theoretical background and some heavy
mathematical machinery in order to get the job done. Often providing improved
results compared with other techniques. The SVMs operate within the framework
of regularization theory by minimizing an empirical risk in a well-posed and
consistent way. [1]. SVMs quickly gained attention from the pattern recognition
community due to a number of theoretical and computational merits. These
include, for example, the simple geometrical interpretation of the margin,
uniqueness of the solution, statistical robustness of the loss function, modularity
of the kernel function, and over fit control through the choice of a single
regularization parameter [2].
In pattern recognition problems, SVMs have been successfully applied to a
number of applications ranging from face detection and recognition, iris detection
and recognition, object detection and recognition, handwritten character and digit
recognition, speaker and speech recognition, information and image retrieval,
prediction and etc. because they have yielded excellent generalization
performance on many statistical problems without any prior knowledge and when
the dimension of input space is very high [3].
1.1. Biometric Overview
Security and the authentication of individuals is necessary for many different
areas of our lives, with most people having to authenticate their identity on a daily
basis; examples include ATMs, secure access to buildings, and international
travel.