生物認證在罪犯蒐證和安全門出入管理上提供很好的技術，例如：虹膜、手形、臉形、手寫辨識、聲紋和指紋辨識。其中，以指紋辨識最常被使用。指紋不但取得方便，其理論和實用也有百年以上的歷史了。但由於指紋辨識系統（Automatic Fingerprint Identification System，AFIS）過程需要比對大量的資料庫，造成花費的時間過於冗長。因此，將指紋進行分類就顯得格外的重要。在論文中，我們實作一個指紋分類系統。
本實驗在兩個不同的資料庫中分別進行，一為FBI所提供的NIST-14資料庫，另一組資料庫則從Live-Scan機器上取得的。實驗所用的機器規格為PIII-733 CPU，256MB RAM及在作業系統Windows 2000 Professional上執行。平均一枚指紋的分類所需時間為1.4秒。在NIST-14 27,000枚資料庫中，正確率為83.13%。如果去除25%品質較差的指紋，正確率可到達88%。而大部份分錯的類別為“Arch”類。而在28枚自行收集的指紋分類結果，正確率為96.4%（只有一枚指紋辨識錯誤）。

指紋分類已經是指紋辨識系統（AFIS）所必要的程序之一。在指紋影像品質良好的情況下，可將大量的指紋資料庫正確的分成四類。而利用奇異點進行分類的方法，必須配合不同的奇異點個數和情況，使用不同的分類依據。實驗中我們發現，當兩個奇異點相距太近，會使得其四周的方向在前處理時遭到破壞，進而無法擷取出來造成分類錯誤。未來解決之後應能增加分類的正確率。

An automatic fingerprint identification system (AFIS) is one of the most important biometric technologies. How to reduce the time of computing in an AFIS with a huge database is an important and necessary issue. Fingerprint classification provides a practical method. In this thesis, we present a fingerprint classification algorithm based on singular points with novel criteria of a classification scheme. A fingerprint is classified into one of the four classes: arch, right loop, left loop, and whorl. The fingerprint classification was tested on 27,000 images in the Nist14 database as well as on 28 images in a live-scan database. The recognition rate of 83.13% for the Nist14 database and 96.4% for the live-scan database have been achieved.

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