An application of fuzzy logic and neural network to fingerprint recognition

Abstract

Because fingerprint patterns are fuzzy in nature and ridge endings are changed easily by scares, we try to only use ridge bifurcation as fingerprints minutiae and also design a fuzzy feature image encoder by using cone membership function to represent the structure of ridge bifurcation features extracted from fingerprint. Then, we integrate the fuzzy encoder with back-propagation neural network (BPNN) as a recognizer which has variable fault tolerances for fingerprint recognition. Experimental results show that the proposed fingerprint recognition system is robust, reliable and rapid.

A Survey on Fingerprint Identification Techniques

“…A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks. According to Ching et.al [44] extracting the proper minutiae from fingerprints is a very important step in identification and may result in poor quality images. The noise in the images can result in poor quality images and cause extraction faults like false minutiae and this cannot be detected correctly.…”