Neural network and, fuzzy logic: an application to fingerprint recognition

Abstract

In this paper, we utilize a feedforward fuzzy neural network to solve the problem of fingerprint-images classification into four classes: Whorl, Left Loop, Right Loop and Arch. At first, each fingerprint image is reduced noise, removed the background and then binarized. Then, one of thinning processes is chosen to perform on the image. Consequently, the 6x5 directional matrix which represents the global flow shapes of the fingerprint is established. After being transformed into 6 x 5 directional matrices, the four matrices of the above classes will be utilized as training patterns for a ANN. After learning from the four training pasterns above, our trial FNN has been experimented through 53 fingerprint samples and results in the classification rate of 96.23%. The obtained testing results demonstrate the system's power of classification.