摘要
近年來生物認證在犯罪蒐證與門禁管理上提供了相當多元的技術, 例如生理上的特徵：臉形、虹膜和指紋與行為上的特徵：聲紋、手寫辨識等。在種類繁多的生物識別技術中，以指紋識別的安全性、便利性與使用成本最為人們所接受。而在資料庫資料量越來越龐大的現今，如何克服龐大的運算量使系統能在短時間內快速找出配對指紋的問題也備受重視，以此為主題之相關研究更是不勝枚舉。自從國際標準組織發佈了指紋特徵檔流通規格至今，絕大部份的分類方法皆基於指紋影像為基礎發展。為了因應發展的驅勢，如何僅根據指紋特徵點進行紋型的分類的確是一大問題。在本文中，我們實作一個基於指紋特徵點的分類系統，即僅以目標指紋特徵點的組成以及由其組成所計算出之關鍵區間內特徵點方向分佈的情況進行指紋分類的方法。其中, 我們以最小平方近似之橢圓包覆整個特徵點集，建立指紋的基本雛型，再以此基準對此作橫斷面紋向掃描，重建出指紋紋路流向，並以此判斷出關鍵區間的範圍，最後以關鍵區間內的特徵點相關統計資訊 進行分析。我們所分的指紋紋型類別一共三類: 右旋紋型、左旋紋型、弓形紋型或螺旋紋型。而以活體指紋影像資料庫FVC2002-db1a 抽取出之ISO格式特徵檔為我們實驗用之資料庫，並經由實驗結果來證明我們方法的可行性。
並列摘要
Fingerprint Classification is a key technique in automatic fingerprint identification systems (AFIS). How to reduce the time of computing in an AFIS with a huge database is an important and necessary issue. Since on July 2006, the international standards organization (ISO) established the standard data format (template) of fingerprint based on minutiae (ISO 19794-2). The minutiae based fingerprint template becomes the international standard of fingerprint authentication/identification systems. However, handling image-based classification system into minutiae-based classification system is still a problem. This thesis present a fingerprint classification algorithm based on minutiae . The fingerprint category is clasified into one of the three classes:right loop, left loop and arch/whorl. Experimental results on live-scan database FVC2002-db1a demonstrate the validity of the proposed algorithm.
參考文獻
[4] International Biometric Group,”The Henry Classification System”, 2003.
[8] George Bebist and Taisa Deaconut andMichael Georgiopoulos,”Fingerprint Identification Using Delaunay Triangulation”,IEEE, 1999.
[9] Jianjiang Feng and Anni Cai,”Fingerprint Indexing Using Ridge Invariants”, The 18th International Conference on Pattern Recognition (ICPR’06), 2006.
[10] Xuefeng Liang, Arijit Bishnu, and Tetsuo Asano,”A Robust Fingerprint Indexing Scheme Using Minutia Neighborhood Structure and Low-Order Delaunay Triangles”,IEEE Transactions On Information Forensics And Security, VOL. 2, NO. 4, December 2007.
[11] Andrew Senior,”A Combination Fingerprint Classifier”,IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 23, NO. 10, October 2001.