自動指紋辨識系統

Automatic Fingerprint Identification System

蔡惠喻(Huei-Yu Tsai)

指導教授 ： 陳朝欽

國立清華大學/電機資訊學院/資訊工程學系/碩士(2008年)

摘要

好幾年來指紋一直被廣泛地用於辨識個人的身份。雖然現在發展不少其它的生物技術，但是指紋資料庫已經在很多國家行之有年，這也是為什麼指紋優於其他生物技術。然而在如此龐大資料庫裡辨識個人身份是困難且耗時間的，因此在這篇論文中，我們實做一套自動指紋辨識系統，它可以分成這些步驟：影像前處理、特徵點擷取、指紋分類和指紋比對。在第一個步驟中，Gabor濾波器用於加強指紋的紋路並減少雜訊，接著擷取特徵點“Endings”和“Bifurcations”。在指紋分類的步驟中，指紋可以分為五大類：(1) Arch、(2) Right Loop、(3) Left Loop、(4) Whorl和(5) Others，這是為了能夠減少指紋比對的時間。最後，我們可以藉由比對兩個指紋的特徵點去計算出比對分數來表示相似度。
這套自動指紋辨識系統測試在六個資料庫上，其中Rindex28和Lindex101這兩個資料庫是從PRIP LAB at NTHU收集得來，另外DB1、DB2、DB3和DB4這四個資料庫由FVC2000提供。此系統執行於Windows XP環境的電腦上(Pentium 4 3.00GHz和1 GB SDRAM)。我們實驗得到的結果分別為100% (112/112), 93.32% (377/404), 97.50% (78/80), 92.50% (74/80), 86.25(69/80) and 92.50% (74/80)。

關鍵字

指紋 ； 特徵點 ； 辨識

並列摘要

Fingerprints have been widely used as personal identification for many years. Although there are several biometric techniques recently, fingerprint still has its own advantage due to large databases which may be established for many years. However, personal identification in such a large database is difficult and time consuming. In this thesis, we implement an automatic fingerprint identification system (AFIS) with these stages: image pre-processing, minutiae extraction, fingerprint classification and fingerprint matching. In the first stage, Gabor filter is used to enhance the furrow and to reduce the noise on the fingerprint image. Then, minutiae (ridge endings and bifurcations) are detected for matching. The stage of fingerprint classification is to reduce the matching time. A fingerprint is classified into one of the five types: Arch, Right Loop, Left Loop, Whorl and Others. In the final stage, a matching score is computed by comparing minutiae patterns between two fingerprint images.
The AFIS is tested on 6 databases of fingerprint images, such as Rindex28, Lindex101 from PRIP Lab at NTHU and DB1, DB2, DB3, DB4 provided by FVC2000, on a PC with Pentium 4 3.00GHz CPU and 1 GB SDRAM running Windows XP. Based on the criterion of reaching top 3 of the matching scores, the identification rates are 100% (112/112), 93.32% (377/404), 97.50% (78/80), 92.50% (74/80), 86.25(69/80) and 92.50% (74/80) by testing the aforementioned fingerprint image databases, respectively.

並列關鍵字

fingerprint ； minutiae ； identification

參考文獻

[Chi05] S.S. Chikkerur, “Online Fingerprint Verification System,” M.S. thesis, State

University of New York at Buffalo, 2005.

[Dau85] J.G. Daugman, “Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters,” J. Optical Soc. Amer. A, Vol. 2, pp. 1,160-1,169, 1985.

[Esp02] V. Espinosa-Duro, “Minutiae detection algorithm for fingerprint recognition”, IEEE Aerospace and Electronics Systems Magazine, Vol. 17, No. 3, 7-10, 2002.

[Hen06] H. Hentous, F. Benhammadi, M. Aissani, and K. Beghad-Bey, “Fingerprint Matching Based on Minutiae Covering Zones”, ISCC, 233-238, 2006.