The extracting correct minutiae from fingerprint images is very important steps in automatic fingerprint identification system. However, the presence of noise in poor-quality images will cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. The ridge minutiae in poor-quality fingerprint images are not always well defined and cannot be correctly detected. Because fingerprint patterns are fuzzy in nature and ridge endings are changed easily by scars, 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. Nowadays, most fingerprint identification systems are based on precise mathematical models, but they cannot handle such faults properly. As we know, human beings are good at recognizing fingerprint pattern. Then, we integrate the fuzzy encoder with back-propagation neural network (BPNN) as a recognizer which has variable fault tolerances for fingerprint recognition. Therefore, a human-like method is applied. This paper presents an adaptive fuzzy logic and neural
network method which has variable fault tolerance. And our experimental results have shown that this fingerprint identification method is robust, reliable, efficiency and our algorithm is faster.
淡江理工學刊 = Tamkang Journal of Science and Engineering 11(4), pp.347-355