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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. |