Fingerprint Identification System Using Artificial Neural Computing Models

Abstract

This thesis presents the idea of features extraction technique of an off-line fingerprint. Traditionally, minutiae, core, delta, crossover, bifurcation, ridge ending, pore, island, enclosure, bridge, dot features of fingerprints are considered to identity a person. These features are available when the fingerprint images are good. The great demerit of those existing techniques is that, they fail to extract the features of poor fingerprint images. If the fingerprint image is blurred then it is difficult to extract those features from the fingerprint. In this research work is fully surveyed all the available methods and different problems related with poor fingerprint identification. Taking into consideration the merits and demerits of the available methods, a new method for feature extraction technique for the poor fingerprint images is proposed. This research is specially dealing with the problem of poorly input fingerprint identification. For these poor types of fingerprint images, features can be extracted by using proposed grid mapping feature extraction techniques. In this process, the fingerprint is processed through Grid Mapping Features (GMF) extraction with fixed square/rectangle cells, such as 16X16 square/rectangle areas. If one small square/rectangle cell of the grid map contains more than 45-55% black pixels, then its digital value is considered as I otherwise 0. These digital values are applied to the input of the neural network for training purpose using Minimum Distance Error Rate Back Propagation (MDER-BP) algorithm, which is proposed in this research work. During the training period, the values of the nodes are updated and stored in a relational knowledge base. The matching part of the system identifies the Fingerprint of a person with the help of the previous experiential values, which were stored in the relational knowledge-base of the system. The proposed Grid Mapping Feature-based fingerprint matching system gives an acceptable accuracy in off-line identification system for poor fingerprint images. It is also shown that the proposed MDER-BP Algorithm also possesses capability for training and matching the poor fingerprint images. Several factors are responsible for getting correct result through neural computing techniques. The convergence of the solution depends heavily on initialization with random numbers and accuracy of the results depends on (i) spread factors (ii) learning rates, (iii) iterations and (iv) hidden units. Finally, it has been concluded that for recognition of fingerprint using the MDER-BP Algorithm the system shows better efficiency with respect to Adaptive Resonance Theory-2 for the poor fingerprint images.