Neural Network

Abstract — The proposed Fingerprint Identification and verification System is biometric identification methodology that uses digital imaging technology to obtain, store, and analyze fingerprint data. Here we want introduced a new method for fingerprint identification technology by minutiae feature extraction using back-propagation algorithm. For an input image, the local ridge orientation is estimated and the region of interest is located. Then, ridges are extracted from the input image, refined to get rid of the small speckles and holes, and thinned to obtain 8-connected single pixel wide ridges. Minutiae are extracted from the thinned ridges and refined using some heuristics. A feature extractor finds minutia features such as ridge end, bifurcation, short ridge and spur from the input fingerprint images. The digital values of these features are applied to input of the neural network for training purpose. For fingerprint recognition, the verification part of the system identifies the fingerprint based training performance of the network. Finally experimental result shows that the number of recognized sample rate of our proposed method is 95 % which is much better than the existing fingerprint verification system using artificial neural network (92.5%).