An application of fuzzy logic and neural network to fingerprint recognition

Abstract

Summary form only given. Correct minutiae extraction is very important in an automatic fingerprint identification system. However, the presence of noise in poor-quality images can cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. Most fingerprint identification systems are based on precise mathematical models, but they cannot handle such faults properly. As human beings are good at recognizing fingerprint patterns, a human-like method is applied. The paper presents an adaptive fuzzy logic and neural network method which has variable fault tolerance. Our experimental results show that this fingerprint identification method is robust, reliable and rapid.