题名

適用於行動運算環境之語句式指紋分類模型的研究

并列篇名

A Study on Syntactic Model of Interpreting Fingerprint Topology for Mobile Computing Environment

作者

張正弘(Jeng-Horng Chang)

关键词

網路身分認證 ； 指紋分類 ； 紋路分佈 ； 語句式模型 ； 拒絕標準 ； network person identification ； fingerprint classification ； ridge distribution ； syntactic model ； rejection standard

期刊名称

德明學報

卷期/出版年月

39卷2期（2015 / 12 / 01）

页次

19 - 36

内容语文

繁體中文
中文摘要

在這篇論文中，我們擬針對無線通訊技術頻寬限制情況下發展合適指紋分析及其分類與辨識相關的技術。因為頻寬受限制，因此接受比對之指紋影像所需傳送之資料量必須做相對應之縮減。同時希望在資料量縮減下，還能保持指紋特徵的不失真。除此之外，行動環境之計算能力相對薄弱，也有必要研發另一套有別於現存之幾何形式指紋特徵比對法，以加快行動身份認證的速度。觀察各類型的指紋，我們發現組成所有指紋的紋路只有十種基本的樣式(fundamental ridges)，而不同類型的指紋，在不考慮紋路的分支(bifurcation)與斷線(fragment)的情形下，都只是由這十種不同的指紋紋路樣式依照特定的順序所組合而成的。基於這個原理，我們提出一種語句式(syntactic)表達法的新模型，以不同的句子來代表個別的指紋，句子中的字母(alphabet)就是那十種指紋紋路的基本形式。並且以此方法為基礎，發展出一個指紋分類系統的新模型。另外，我們也嘗試利用紋路分佈所具備的特質來定義出一個清楚的拒絕分類標準。這個分類法除了能完成Henry分類的那七種有效分類以外，也可以使資料庫中的子集合數目大量增加。對於類似全民指紋建檔等的超大型指紋資料庫，或許才可能真正落實。
英文摘要

In this paper, a syntactic model for interpreting fingerprint structure which can be use in a mobile computation environment is proposed. Because bandwidth is limited, therefore the amount of fingerprint image data transfer must be reduced. From our observation, there exist only ten basic ridge patterns which constructing fingerprints. Fingerprint classes can be interpreted as a combination of these ten ridge patterns with different ridge distribution sequence. The classification task is performed depending on the global distribution of the ten basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. This classification scheme can accomplish the seven-classes discrimination of the Henry's classification. Moreover, the number of the subsets in database can be increased largely by combining the concept of structural classification and the number of ridges. For this reason, we will design the ridge number calculation and the singular point detection method in gray-scale domain. We also will define a rejection standard by the appearance of the type line.
主题分类 人文學 > 人文學綜合
基礎與應用科學 > 資訊科學
社會科學 > 社會科學綜合
参考文献
  1. Ferenc, Bodon. (2006). A Survey on Frequent Set Mining, Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Budapest.
  2. Blue, J. L.(1994).Evaluation of Pattern Classifiers For fingerprint and OCR Application.Pattern Recognition,27(4),485-501.
  3. Cao, Kai,Pang, Liaojun,Liang, Jimin,Tian, Jie(2013).Fingerprint classification by a hierarchical classifier.Pattern Classification,46(12),3186-3197.
  4. Chang, Jeng-Horng,Fan, Kuo-Chin(2001).Fingerprint Ridge Allocation in Direct Gray-Scale Domain.Pattern Recognition,34(10),1907-1925.
  5. Chang, Jeng-Horng,Fan, Kuo-Chin(2002).A New Model for Fingerprint Classification by Ridge Distribution Sequences.Pattern Recognition,35(6),1209-1223.
  6. Chong, M. M. S.,Ngee, T. H.,Jun, L.,Gay, R. K. L.(1994).Geometric Framework for Fingerprint Image Classification.Pattern Recognition,30(9),1475-1488.
  7. Jain, A. K.,Prabhaker, S.,Hong, L.(1999).A Multichannel Approach to Fingerprint Classification.IEEE Trans. Pattern Analysis Mach Intell.,21(4),348-359.
  8. Karu, K.,Jain, A. K.(1996).Fingerprint Classification.Pattern Recognition,29(3),389-404.
  9. Kovacus-Vajna, Z. M.(2000).A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping.IEEE Trans. Pattern Analysis Mach Intell.,22(11),1266-1276.
  10. Luping, Ji,Zhang, Yi(2007).SVM-based Fingerprint Classification Using Orientation Field.3rd International conference on Natural Computation,2,724-727.
  11. Miao, D.,Maltoni, D.(1997).Direct Gray-Scale Minutiae Detection In Fingerprints.IEEE. Trans. Pattern Analysis Mach. Intell.,19(1),27-39.
  12. Ratha, N. K.,Karu, K.,Chen, S.,Jain, A. K.(1996).A Real-Time Matching System for Large Fingerprint Database.IEEE Trans. Pattern Analysis Mach. Intell.,18(8),799-813.
  13. Wei, L.,Yonghui, C.,Fang, W.(2008).Fingerprint Classification by Ridgeline and Singular Point Analysis.Congress on Image and Signal Processing
  14. Yao, Y.(2001).A new machine learning approach to fingerprint classification.Congress of the Italian Association for Artificial Intelligence (7th)
  15. Yazdi, M.,Gheysari, K.(2008).A New Approach for the Fingerprint Classification Based on Gray-Level Co-Occurrence Matrix.Proceedings Of World Academy Of Science, Engineering And Technology
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