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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/50559

    题名: A Fingerprint Identification System Based on Fuzzy Encoder and Neural Network
    作者: 謝景棠;胡家幸
    贡献者: 淡江大學電機工程學系
    关键词: Fingerprint Identification;Image Analysis;Fuzzy System;Neural Networks
    日期: 2008-12
    上传时间: 2010-08-09 19:48:42 (UTC+8)
    出版者: 新北市:淡江大學
    摘要: 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.
    關聯: 淡江理工學刊 = Tamkang Journal of Science and Engineering 11(4), pp.347-355
    DOI: 10.6180/jase.2008.11.4.04
    显示于类别:[電機工程學系暨研究所] 期刊論文


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