Fingerprint Matching of Normalized Image based on Euclidean Distance

Abstract

Euclidean distance is one of the oldest methods for mapping distance between two points. It is highly demandable for matching process. Recently there are many techniques for matching fingerprints. Using Euclidean distance, minutiae based fingerprint matching gives accurate matching results. Euclidean Distance is a distance matching technique which is broadly perusal in computational geometry, image processing, computer graphics and pattern recognition. According to the Euclidean distance formula, simply in the plane the distance between two points is map, and the resulting distance is match with the resulting distance of reference fingerprint for matching. Normalization is significant enhancement technique that applied to renovate the contrast in an image. In the case of noisy fingerprint images, normalization is quite important technique for better and accurate outcomes. This paper deals with to perform Euclidean distance between minutiae points for provide robustness of our algorithm for matching fingerprints to reference fingerprint. The process of determining Euclidean distance is done by a tool of Image processing i.e. Matlab.