Author
Abstract
Fingerprint authentication and recognition is an important subject that has been widely used in various applications because of its reliability and accuracy in the process of authenticating and recognizing the person's identity. In this paper, an Intelligent Fingerprint Authentication Model (IFAM) based upon the neural network has been proposed. The proposed work consists of two main phases which are the features extraction and the authentication. The features extraction phase has been regarded via proposing a statistical and geometrical approach for determining and isolating the features of the fingerprint images. The proposed approach is called the Features Ring Approach which is abbreviated by FRA. The approach creates a circular ring centered at the core point of the fingerprint to bind the valuable features that are invariant under rotation and translation. The radius of the outer circle of the ring is suggested to be variable to give a variable area for the established circular ring.
The authentication phase of IFAM suggests the neural network to hold the job of verification of the extracted feature patterns resulted by FRA for a fingerprint image of certain person. This is done using a neural network trained with a collection of features patterns extracted from fingerprint images. Backpropagation (BP) is suggested as a training algorithm for the structured neural network.
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