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系統識別號 U0002-1307201016405200
中文論文名稱 灰階演算之自動指紋辨識系統
英文論文名稱 Automatic Fingerprint Identification System Based on Direct Gray-Scale Image Processing
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 98
學期 2
出版年 99
研究生中文姓名 劉哲瑋
研究生英文姓名 Che-Wei Liu
學號 691380108
學位類別 博士
語文別 英文
口試日期 2010-06-15
論文頁數 102頁
口試委員 指導教授-黃聰亮
委員-黃聰耀
委員-蕭瑛東
委員-翁慶昌
委員-陳友倫
中文關鍵字 指紋 
英文關鍵字 Fingerprint classification  fingerprint matching 
學科別分類
中文摘要 中文摘要:
指紋一直以來都是最為大眾所接受,也能成為法庭正式證據的生物特徵,甚至現在很多家庭或是公司的門禁管制系統,也都是採用指紋辨識模組,就是因為指紋的獨特性以及不變性,即使年紀變化或是一般受傷磨損都不會改變指紋特徵,讓指紋可以成為個人的身分表徵。
數百年前即有人開始發現指紋的特殊性,從Henry於1888提出指紋分類法之後,系統化的指紋辨識方法開始為大家所研究,各式各樣的指紋相關理論也蓬勃發展,包括指紋分類法、指紋流向、指紋特徵、指紋比對等專題也被廣泛討論。
在這本論文中,我們提出了一個完整的自動指紋辨識流程,從一開始的指紋前處理:背景去除、正規化、計算指紋流向以及頻率、奇異點的搜尋,到指紋的分類、特徵點搜尋以及比對,在這些處理流程中,我們不只參考了一些其他學者的研究,更提出了對這些流程的改善或是全新的演算法。
在指紋前處理部分,我們提出了”少即是多”的概念,就是在指紋區塊中占較小比例的圖形,反而可以決定該指紋區塊的特性,例如當指紋按壓太大力時,指紋的脊線會變得太粗、甚至會連結在一起,這時候脊線的像素會佔據大部分的區域,我們去搜尋這些脊線時會得到很多的假特徵點,因此我們反過來強化那些較少的訊號區域,就是谷線部分,反而可以得到比較正確的特徵訊息。
在指紋分類方面,我們參考資料探勘的概念,提出了一套新的指紋分類法,可以搭配Henry的分類法,也可以單獨運作。根據統計,九成五以上的指紋有上方核心點,我們利用上方核心點附近的指紋頻率變化,來進行指紋分類以及檢索,指紋會依照頻率變化被分成37類,在檢索時採用階層式架構,由機率最大的指紋分類開始比對,逐步往符合機率較低的類別進行搜尋。這個方式改善了傳統非黑即白的架構,傳統方法裡若一開始分類錯誤,就幾乎要搜尋整個資料庫來尋找可能符合的指紋。但是在我們提出的方法中,卻是由機率最高的類別逐步往機率較低的類別進行搜尋,這種階層式架構改善了指紋檢索的效率,在我們的驗證中,在第三階的分類索引上就可以達到90%以上的正確率了。
在特徵點擷取部分,我們不用傳統的二值化方法來進行搜尋,而是利用直接灰階演算的方式來進行特徵擷取,因為灰階圖形保留了較多的指紋資訊,在一些汙損或是模糊的指紋當中,進行二值化動作會產生大量的噪點以及假點,而且二值化過後,指紋圖形原始的灰階分布已經被破壞,產生錯誤時比較難以挽救,只能重新進行處理,在我們提出的方法中,我們會採用二階段搜尋,先利用灰階統計方式標出可能的脊線以及谷線,再引用” 少即是多”的概念,
由較少的圖形來強化較多的部分,最後將這些點連接之後,即可進行指紋特徵擷取。
在指紋比對部分,除了套用之前的指紋分類檢索規則之外,我們提出極座標比對法,利用前對位的方式校正指紋
`,再根據其特徵點分布進行比對,這種採用前對位的比對方式非常快速,我們可以在一秒內比對超過一萬個指紋,而同時保有相當的準確率。
最後我們會討論一些模糊或是片斷的指紋上所遭遇的問題以及未來可能的發展方向,並且提供我們進行研究階段的心得以及設計方針,提供後續研究者參考。
英文摘要 Abstract:
Fingerprint is the most popular biometric feature that is widely accepted by the public. Fingerprint is also adopted by the court to be the forensic evidence. Even now, a lot of family and company use a fingerprint recognition system as the access control system. Fingerprints possess two characteristics: Uniqueness and Invariance. The age and the damage on the finger didn’t change the pattern of fingerprint. Therefore, fingerprint can be the identity of individual.
Fingerprint had been researched for several hundred years. The first systematical classifying method of fingerprint was proposed by Henry in 1888. After Henry’s approach, many researchers dedicated on the systematic processing of fingerprint recognition, every kind of theorems and algorithms were developed to solve several fingerprint issues: fingerprint classification, fingerprint orientation fields, fingerprint feature and minutiae, and fingerprint matching.
In this dissertation, we propose a complete automatic fingerprint identification system from the pre-processing: segmentation, normalization, orientation and frequency estimation and singular points extraction. We also propose a new fingerprint classification method, and direct grey scale minutiae detection in fingerprint and the fingerprint matching. We are not only referring some researches in the literature, we also improve the original procedure and propose some new algorithm in the fingerprint processing`.
In the fingerprint pre-processing, we bring up the “Less is More” concept to the fingerprint. The significance of this concept is that pattern possess small portion in the fingerprint block would decide the characteristic of fingerprint texture. As an instance, if a finger pressed too hard on the fingerprint acquiring sensor, we will acquire a smudged fingerprint. The width of ridge lines will become too wide even can connect to other ridges. In this situation, the pixels belong to the ridge line take large portion in the fingerprint pattern. If we trace the ridge lines in the fingerprint, we will extract a lot of false minutiae. Hence, we enhance the information that takes less part of fingerprint which is the valley line in this case, and then we can extract the fingerprint features more correct.
In the fingerprint classification, we illustrate the concept form data-mining to propose a new fingerprint classifying and indexing method. Our method can co-operate with Henry’s classification or work individually. By the statistical data of fingerprints, over 95 percents of fingerprint contain an upper core. We calculate the frequency around the upper core and classify fingerprints to 37 classes. We design a hierarchical structure for fingerprint indexing form the class that has the largest probability of matching to those classes that have smaller probability. Our method improves the traditional classification that we have to search the entire database while the input fingerprint is incorrectly classified. We arrange those classes in the different indexing level from high probability to low. In our fingerprint indexing verification, we can acquire more than 90% of matching rate in the first three levels.
In the fingerprint minutiae extraction, we didn’t use traditional binarizing method to detect the minutiae, but direct extract minutiae on the grey scale image. Because the grey level image contains more information about fingerprint texture and significant amount of information may be lost during the binarization process. The binarization and thinning are time consuming; the thinning process may generate a large number of spurious minutiae. In the absence of an a priori enhancement step, most of the binarization techniques do not provide satisfactory results when applied to low-quality images. We propose two level minutiae extracting process, in the first level; we mark the peak and trough on the fingerprint histogram to indicate the position of ridge lines and valley lines. And we employ the “Less is more” concept that those textures will be enhanced by the pattern that takes fewer portions. In the second level, we link those points to extend the ridge lines and valley lines and then we can extract minutiae form the trace of ridge lines.
In the fingerprint matching process, we proposed the polar coordinate system to represent the minutiae of fingerprint and match those minutiae with a pre-alignment process. This matching algorithm is very fast that we can match more than 10000 fingerprint templates within one second. In the same time, we still can keep good accuracy of fingerprint matching.
In the last chapter, we discuss some issues about the processing of blurred or fragmental fingerprint and the future works. We also share some experience and the strategy that we design this system.
論文目次 TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION……………………………….……..1
1.1 Introduction…………………………………………………………….1
1.2 Biometric Systems…………………………………………………..….2
1.2.1 Biometric features……………………………………………….3
1.2.2 Biometric recognition system……………………….………….7
1.3 System Overview……………………………………………………….9

CHAPTER 2 FINGERPRINT SYSTEM……………………………..….11
2.1 History of Fingerprints………………………………………..…...….11
2.2 Fingerprint Acquisition…………………………………………….….12
2.2.1 Optical sensors………………………………………………….13
2.2.2 Silicon sensors……………………………………………….….15
2.2.3 Ultrasonic sensors………………………….……………………16
CHAPTER 3 FINGERPRINT PREPROCESSING…………………….19
3.1 System Schema……………………………………………...……...….19
3.2 Segmentation……………………………………………….………….23
3.3 Normalization……………………………………………...…….…….28
3.4 Orientation Estimation…………………………………………...…….39
3.5 Frequency Estimation…………………………………….……...…….44
3.6 Singular Point Detection……………………………………………….45
CHAPTER 4 FINGERPRINT CLASSFICATION…….……………….53
4.1 Introduction……………………………………………………...…….53
4.2 Fingerprint Classification………………………………………..…….57
4.3 Fingerprint Indexing……………………………………………..…….63
CHAPTER 5 FEATURE EXTRATION AND MATCHING………….71
5.1 Introduction……………………………………………………...…….71
5.2 Minutiae Extraction………………………………………………...….72
5.2.1 Ridges and Valleys Estimation………………………………….73
5.2.2 Ridges and Valleys Extension……………………..…………….76
5.2.3 Minutiae Extraction…………………………….……………….81
5.2.4 Minutiae Filtering…………………………………..…………..84
5.3 Fingerprint Enrollment and Matching………………….……………..86
CHAPTER 6 EXPERIMENTAL RESULT…………………………….90
6.1 Database……………………………………………………..……….90
6.2 Classification………………………………...………….………….90
6.3 Matching………………………………………………..…………….94
CHAPTER 7 CONCLUSION…………………...……………………….101
REFERENCES…………………………………………….………..103

LIST OF FIGURES

1.1 Biometric system……………..…………………………………………………………….2
1.2 Face Geometry……………………...…………………………………………………3
1.3 Vein prints of finger and hand. ……………………………………….……………………4
1.4 Iris features. ……………………………………………………………………..…………4
1.5 Retina features. ……………………………………………………………………….……5
1.6 Sound waves. …………………………..………..…………………………………5
1.7 Fingerprint acquisition. ……………………………………………………………6
1.8 Multiple biometric matching. ………………………..……………………………7
1.9 FAR and FRR for a specific threshold. …………………………………………………8
1.10 An example of FAR and FRR. …………………………………………………8
1.11 System flowchart. …………………………………..………….………………10
2.1 A frustrated total internal reflection optical sensor. …………..…………………13
2.2 FTIR with a sheet prism. ……………………………………………..………13
2.3 A sensor based on optical fibers. …………………………………………………14
2.4 Electro optical fingerprint sensor. …………………………………………………14
2.5 Capacitive sensing. ………………………………………………………15
2.6 An example of thermal sensor. …………………………………………………16
2.7 The basic principle of the ultrasound technique. ……………………………17
2.8 Wet fingerprint images acquire by different sensors……………………………17
2.9 Marker fingerprint images acquire by different sensors……………………………17
3.1 Fingerprint processing flowchart. …………………………………………………22
3.2 (a) original image; (b) variance field; (c) quality image derived from the variance field; (d) segmented image. ………………………………………………………………..………23
3.3 (a) a noisy fingerprint image; (b) result of segmentation. ……………………………25
3.4 (a) a noisy background block; (b) background block after the low pass filter; (c) a foreground block; (d) foreground block after the low pass filter. ……………………………25
3.5 (a) original fingerprint image; (b) fingerprint after the low pass filter; (c) result of fingerprint segmentation. ………………………………………………….………26
3.6 Some examples of fingerprint segmentation and regularization………………………27
3.7 (a) a bright fingerprint; (b) an over pressed fingerprint. ……………………………28
3.8 (a) a fingerprint image with good contrast; (b) the same fingerprint appears in bad contrast. ……………………………………………………………………………………29
3.9 Histograms of different fingerprint images. ……………….…………………30
3.10 Left is origin image; center is binarization image; right is normalization images…32
3.11 Left is origin image; center is binarization image; right is normalization images…33
3.12 The comparison of common normalization and our purpose, left side is the original image, results of traditional normalization in the middle, results of our purpose are in the right side. …………………………………………………………………..………………………34
3.13 Left sides is the original image, results of traditional normalization in the middle, results of our purpose are in the right side. ……………………………………………………35
3.14 User’s finger press (a) too light; (b) too hard. ……………..…………………36
3.15 (a) a over pressed fingerprint; (b) its reverse image; (c) a tiny pressed fingerprint; (d) its reverse image. ……………………………………………………………………37
3.16 (a) a ridge based fingerprint block; (b) its valley extension; (c) a valley based fingerprint block; (d) its ridge extension. ……………………………………….………………38
3.17 Eight fingerprint orientations. ……………………………..………………39
3.18 Spatial masks of eight orientations. …………………………………………40
3.19 Surface plot of an orientation mask. …………………………………………………40
3.20 Examples of fingerprint orientation estimation and regularization. ………………41
3.21 Orientation field modifications (a) before orientation field modification; (b) after orientation field modification; (c) after orientation field modification with the original fingerprint image. ………………………………………………………………..……43
3.22 The ridge frequencies in different region are obviously different. …………………44
3.23 (a) upper core; (b) downer core; (c) delta point; (d) a fingerprint contain three singular points. …………………………….………………………………….…………45
3.24 The Poincaré index around curve C.. ……………………………….…………46
3.25 Examples of Poincaré index belonging to a whorl, loop, and delta singularity.47.
3.26 Singularity detection using Poincaré index…………….……………………….47
3.27 The Searching result of Poincaré index method in a poor quality fingerprint…....48
3.28 Gray scale surface of upper core. …………….……..……………………………49
3.29 The flowchart of detection of the upper core point. ……….…………………50
3.33 (a) the ant positions are labeled as ○; (b) the traces of search are labeled as ○ that ant search local solution from initial position and iterate search procedure; (c) the upper core point is labeled as ○. …………..……………………………………………………51
3.34 The results of upper detection. …………………….………………….…………52
4.1 The five commonly used fingerprint classed: (a) left loop; (b) right loop; (c) plain whorl; (d) arch; (e) tented arch; (f) twin loop whorl. ……………………………54
4.2 Fingerprints belonging to different classes that have similar appearance….56
4.3 Fingerprints belonging to the same class that have different characteristics……56
4.4 Examples of noisy fingerprint images. ………………………..…………………56
4.5 (a)(b)(c) are the same fingerprint input in different time; (d)(e)(f) are another fingerprint acquired in different time. …………………………...……………………………………57
4.6 (a) the surface plot of Gabor filter; (b) its gray scale mask. ……………………59
4.7 Fingerprints after Gabor filtering, left side are their original images. …………………59
4.8 An example of our purposed method of classification. ………….…………………61
4.9 Some classification results: (a) Rs; (b) Lu; (c) UL; (d) sr. ……………..………………62
4.10 The example of Indexing process as the Input fingerprint classified to class “RL”, which contain two capital letters. The number in the brackets is the class matching score….65
4.11 The example of Indexing process as the Input fingerprint classified to class “rL”….66
4.12 The example of Indexing process as the Input fingerprint classified to class “Rs”….67
4.13 The example of Indexing process as the Input fingerprint classified to class “rl”…68
4.14 The example of Indexing process as the Input fingerprint classified to class “rs”….69
4.15 The example of Indexing process as the Input fingerprint classified to class “ss”….70
5.1 Different fingerprint minutiae types. …………………………..…………72
5.2 Histogram of the scan line. ………….………………………………………….…72
5.3 A fingerprint histogram that is not easy to distinguish ridge and valley. ……………73
5.4 The 2-Dimension Gaussian filter and its gray level image. ……………………74
5.5 (a) original histogram; (b) histogram after Gaussian filter. ……………………74
5.6 An Example of fingerprint ridge detection: (a) is the original image; (b) is the result of ridge extraction, white spot denotes the center of ridges. ………….…………75
5.7 Histograms of three scan lines on a crumbly fingerprint. ……………….………………76
5.8 A comparison of different fingerprint type: (a) histogram of a ridge-based fingerprint; (b) histogram of a valley based fingerprint. ……………………..………………………77
5.9 Ridge and valley extraction form different fingerprint, we plot white spots on the ridge line and black spots on the valley line. ……………………………………….………78
5.10 An example of ridge extraction and valley extraction of a latent fingerprint….79
5.11 The linked result of ridge line extraction. (a) is a valley based fingerprint;(c) is a ridge based fingerprint. ………………………………….…………………………………80
5.12 Minutiae in the same fingerprint will appear in different types, ridge terminations in (a) looks like ridge bifurcation in (b). ……………………………………………………81
5.13 The direction of minutiae: (a) is a bifurcation; (b) is a termination. ……………………82
5.14 Ridge frequencies were varied around a bifurcation; yellow spots denote the valleys and blue spots mark the position of ridges. ……………………………….……………82
5.15 The position of the fingerprint minutiae. …………………..…….…………83
5.16 The filtering rules to eliminate incorrect minutiae. …………………………84
5.17 (a) the original fingerprint image; (b) the result of minutiae extraction; (c) the minutiae filtering result. ………………………………………….……………………85
5.18 Pre-alignment process (a) is the original pattern; (b) is rotated to regular angle………..86
5.19 The coordinate system of fingerprint template. ……………………..…………87
5.20 The range of a fingerprint. ……………….…………..…………………………88
6.1 The same fingerprint that classify to different class (“UL” and “lU”). …………………94
6.2 The curve of false acceptance rate and the false rejection rate. ……………………95
6.3 Some fingerprints that are difficult to extract the features. ……………………96
6.4 The FAR and FRR curve of FVC2000 database1……………………….………………..97
6.5 The FAR and FRR curve of FVC2000 database2. …………………….………………..97
6.6 The FAR and FRR curve of FVC2000 database3. …………………….………………..98
6.7 The FAR and FRR curve of FVC2000 database4. …………………….………………..98
6.8 The FAR and FRR curve of FVC2002 database1. …………………….………………..99
6.9 The FAR and FRR curve of FVC2002 database2. …………………….………………..99
6.10 The FAR and FRR curve of FVC2002 database3. …………………….……………..100
6.11 The FAR and FRR curve of FVC2002 database4. …………………….……………..100


LIST OF TABLES

2.1 The comparison of different fingerprint acquiring devices. …………………………18
4.1 Singular points in the five fingerprint classes. ……………………………………….56
4.2 The classes name of our purposed classification system. ………………………………61
6.1 Result of classification. The proportions of different class are listed in green grid. The accuracy rates of indexing o1f different level are listed in blue grid…………………92
6.2 Result of classification2. The proportions of different class are listed in green grid. The accuracy rates of indexing of different level are listed in blue grid…………………..93
6.3 The matching rate of each level. ……………………….………………………….93
6.4 Classifying result of different type of classes, where X is capital letter and x is lowercase letter………………………………………………………………………………………….94
參考文獻 [1] A.K. Jain, R. Bolle, S. Pankanti (eds), Biometrics: Personal Identification in Networked Society, Kluwer Academic, December 1998.
[2] P. Kalocsai, C. von der Malsburg, and J. Horn, “Face recognition by statistical analysis of feature detectors”, Image and Vision Computing, vol.18, no.4, pp. 273-278, 2000.
[3] L. Hong and A.K. Jain, “Integrating faces and fingerprints”, IEEE Transactions Pattern Analysis Machine Intelligent, vol. 20, no. 12, pp. 1295-1307, 1998.
[4] T. Hamada, K. Kato, and K. Kawakami, “Extracting facial features as in infants”, Pattern Recognition Letters, vol.21, no.5, pp. 407-412, 2000.
[5] V. Nalwa, “Automatic on-line signature verification”, Proceedings of the IEEE, vol. 85, no. 2, pp. 213-239, 1997.
[6] A.K. Jain, A. Ross and S. Pankanti, “A Prototype Hand Geometry-based Verification System”, 2nd Int'l Conference on Audio- and Video-based Biometric Person Authentication, Washington D.C., pp. 166-171, 1999.
[7] J.G. Daugman, “High confidence visual recognition of persons by a test of IEEE Transactions Pattern Analysis Machine Intelligent, vol. 15, no.11, pp. 1148-1161, 1993.
[8] J.G. Daugman, “Neural image processing strategies applied in real-time pattern recognition”, Real-Time Imaging, vol. 3, pp. 157-171, 1997.
[9] S. Furui, “Recent Advances in Speaker Recognition”, Pattern Recognition Letters, vol. 18, pp. 859-872, 1997.
[10] U. Halici, L.C. Jain, and A. Erol, “Introduction to fingerprint recognition”, Intelligent Biometric Techniques in Fingerprint and Face Recognition, pp. 137-151, CRC Press, Boca Raton, Florida, 1999.
[11] American National Standards Institute, Fingerprint Identification – Data Format for Information Interchange, New York, 1986.
[12] R.A. Baxter, “SAR image compression with the Gabor transform”, IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no.1, pp. 574-588, 1999.
[13] T.T. Chinen and T.R. Reed, “A performance analysis of fast Gabor transform Graphical Models and Image Processing, vol.59, no.3, pp. 117-127, 1997.
[14] D. Maio and D. Maltoni, "Direct gray-scale minutiae detection in fingerprints", IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 19, no. 1, pp. 27- 40, 1997.
[15] J.G. Daugman, “Two-dimensional spectra analysis of cortical receptive field profiles”, Vision Research, vol.20, pp. 847-856, 1980.
[16] L. Hong, “Automatic personal identification using fingerprints”, PhD Thesis, Michigan State University, 1998.
[17] Federal Bureau of Investigation, The Science of Fingerprints: Classification and Uses, U.S. Government Printing Office, Washington, D. C., 1984.
[18] Federal Bureau of Investigation, “WSQ gray-scale fingerprint image compression specification,” IAFIS-IC-0110v2, 1993.
[19] L. Hong, A.K. Jain, S. Pankanti, and R. Bolle, “Fingerprint enhancement”, Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp. 202-207, 1996.
[20] H.C. Lee and R.E. Gaensslen, Advances in Fingerprint Technology, Elsevier Publishing, 1991.
[21] A. Moenssens, Fingerprint Techniques. Chilton Book Company, London, 1971.
[22] F. Galton, Finger Prints, Mcmillan, London, 1892.
[23] E.R. Henry, Classification and Uses of Finger Prints, Routledge, London, 1900.
[24] N.K. Ratha, J.H. Connell, and R.M. Bolle, “Image mosiacing for rolled fingerprint construction”, Proceedings of 14th International Conference on Pattern Recognition, vol. 2, pp. 1651–1653, 1998.
[25] Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani, and S. Tomita, “A Gabor filter-based method for recognizing handwritten numerals”, Pattern Recognition, vol. 31, no. 4, pp. 395-400, 1998.
[26] D.Maltoni, D.Maio, A.K. Jain and S. Prabhakar, Handbook of fingerprint recognition, Springer, 2003.
[27] C.I. Watson, and C.L. Wilson, “NIST Special Database 4, Fingerprint Database,” U.S. National Institute of Standards and Technology, 1992.
[28] C.I. Watson, “NIST Special Database 14, Fingerprint Database,” U.S. National Institute of Standards and Technology, 1993.
[29] R.C. Gonzales and R.E. Woods, Digital Image Processing, 3rd edition, Prentice-Hall, Englewood Cliffs, NJ, 2007.
[30] M. Kass and A. Witkin, “Analyzing oriented patterns,” Computer Vision Graphics and Image Processing, vol. 37, no. 3, pp. 362–385, 1987.
[31] L. Hong and A.K. Jain, “Integrating Faces and Fingerprints for Personal Identification,” in Proc. Asian Conf. Computer Vision, 1998a.
[32] M. Kawagoe and A. Tojo, “Fingerprint pattern classification,” Pattern Recognition, vol. 17, pp. 295–303, 1984.
[33] M. Dorigo, V. Maniezzo and A. Colorni, The Ant System: An Autocatalytic Optimizing Process, Technical Report No. 91-016 Revised, Politecnico di Milano, Italy, 1991.
[34] M. Dorigo, V. Maniezzo and A. Colorni, “The Ant System: Optimization by a Colony of Cooperating Agents”, IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol. 26,No. 1, pp. 29-42, 1996.
[35] R. Cappelli, D. Maio and D. Maltoni, “Fingerprint Classification Based on Multi-space KL,” in Proc. Workshop on Automatic Identification Advances Technologies, pp. 117–120, 1999
[36] C.L. Wilson, G.T. Candela and C.I. Watson, “Neural network fingerprint classification,” Journal of Artificial Neural Networks, vol. 1, no. 2, pp. 203–228, 1994.
[37] A.K. Jain, S. Prabhakar, and L. Hong, “A multichannel approach to fingerprint classification,” IEEE Transactions Pattern Anal. Machine Intell., vol. 21, no. 4, pp. 348–359, 1999.
[38] A.K. Jain, S. Prabhakar, and L. Hong, and S. Pankanti, “FingerCode: a filterbank for fingerprint representation and matching,” Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition(CVPR), vol. 2, pp. 187-193, 1999.
[39] A.K. Jain, S. Prabhakar, and L. Hong, and S. Pankanti, “Filterbank-based fingerprint matching,” IEEE Transactions Image Processing, vol. 9, no. 5, pp. 846-859, 2000.
[40] Z.M. Kovacs-Vajna, “A fingerprint verification system based on triangular matching and dynamic time warping,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 22, pp. 1266–1276, 2000.
[41] A.K. Jain, S. Prabhakar and S. Chen, “Combining Multiple Matchers for a High Security Fingerprint Verification System”, Pattern Recognition Letters, vol. 20, no. 11-13, pp.1371-1379, 1999.
[42] Y. Hamamoto, “A Gabor filter-based method for fingerprint identification”, in L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, and S. Tsutsui, editors, Intelligent Biometric Techniques in Fingerprint and Face Recognition, pp. 137-151, CRC Press, Boca Raton, Florida, 1999.
[43] J.G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters”, J. Opt. Soc. Amer. A, vol. 2, no. 7, pp. 1160-1169, 1985.
[44] L. Hong, Y. Wan, and A.K. Jain, “Fingerprint image enhancement: algorithms and performance evaluation”, IEEE Transactions Pattern Analysis Machine Intelligent, vol. 20, no. 8, pp. 777-789, 1998.
[45] A.K. Hrechak and J.A. McHugh, “Automated fingerprint recognition using Pattern Recognition, vol. 23, no. 8, 1990.
[46] L. Coetzee and E.C. Botha, “Fingerprint recognition in low quality images”, Pattern Recognition, vol. 26, no. 10, pp. 1441-1460, 1993.
[47] D.C. Hung, “Enhancement and feature purification of fingerprint images”, Pattern Recognition, vol. 26, pp. 1661-1671, 1993.
[48] A. Ibrahim and M.R. Azimi-Sadjadi, “A fast learning algorithm for Gabor transformation”, IEEE Transactions on Image Processing, vol.5, no.1, pp.171-175, 1996.
[49] A.K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[50] A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, ‘On-line fingerprint verification’, IEEE Transactions Pattern Analysis Machine Intelligent, vol. 19, no. 4, pp. 302-314, 1997.
[51] A.K. Jain, N.K. Ratha, and S. Lakshmanan, “Object detection using Gabor filters”, Pattern Recognition, vol. 30, no. 2, pp.295-309, 1997.
[52] A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, “An identity-authentication system using fingerprints”, Proceedings of the IEEE (Special Issue on Automated Biometrics), vol. 85, no. 9, pp. 1365–1388, 1997.
[53] A.K. Jain, S. Prabhakar, and L. Hong, “A multichannel approach to fingerprint classification”, IEEE Transactions Pattern Analysis Machine Intelligent, vol. 21, no. 4, pp. 348 -359, 1999.
[54] A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, “Filterbank-based fingerprint matching”, IEEE Transactions on Image Processing, vol. 9, no. 5, pp. 846-859, 2000.
[55] A.K. Jain, S. Prabhakar, and S. Pankanti, “Twin Test: On Discriminability of Proc. 3rd International Conference on Audio- and Video-Based Person Authentication, Sweden, June 6-8, 2001.
[56] K. Karu and A.K. Jain, “Fingerprint classification”, Pattern Recognition, vol. 29, no. 3, pp. 389-404, 1996.
[57] M. Kawagoe and A. Tojo, “Fingerprint pattern classification”, Pattern Recognition, vol. 17, no. 3, pp. 295-303, 1984.
[58] L. Lam, S.W. Lee, and C.Y. Suen, "Thinning methodologies: A comprehensive survey", IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 14, no. 9, pp. 869-885, 1992.
[59] P. Lau, N.P. Papanikolopoulos, and D.L Boley, “Gabor-QR decomposition for image encoding,” Electronics Letters, vol.29, no.25, pp. 2182-2183, 1993.
[60] P. Lau and N.P. Papanikolopoulos, “Adaptive Gabor image transformation, application in scientific visualization”, Proceedings of International Conference on Image Processing, vol.3, pp. 504-507, 1995.
[61] J.F. Mainguet, M. Pégulu, and J.B. Harris, “Fingerprint recognition based on silicon chips”, Future Generation Computer System, vol. 16, pp. 403-415, 2000.
[62] D. Maio and D. Maltoni, “Ridge-line density estimation in digital images”, Proceedings of 14th ICPR, Brisbane Australia, pp. 534-538, 1998.
[63] D. Maio, D. Maltoni, R. Cappelli, J.L. Wayman, and A.K. Jain, “FVC2000: Fingerprint verification competition”, Proceedings of ICPR2000, 15th Int. Conf. Pattern Recognition, Barcelona, Spain, 2000.
[64] B.M. Mehtre and B. Chatterjee, “Segmentation of fingerprint images A composite method”, Pattern Recognition, vol. 22, no. 4, pp. 381–385, 1989.
[65] B. Miller, “Vital signs of identity,” IEEE Spectrum, vol. 31, no. 2, pp.22-30, 1994.
[66] B. Moayer and K.S. Fu, “A syntactic approach to fingerprint pattern Pattern Recognition, vol. 7, pp. 1-23, 1975.
[67] B. Moayer and K.S. Fu, “A tree system approach for fingerprint pattern IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 8, no. 3, pp. 376-388, 1986.
[68] H. Morimura, S. Shigematsu, and K. Machuida, “A novel sensor cell architecture and sensing circuit scheme for capacitive fingerprint sensors”, IEEE Journal of Solid-State Circuits, vol. 35, no. 5, 2000.
[69] L. O’Gorman and J.V. Nickerson, “An approach to fingerprint filter design”, Pattern Recognition, vol. 22, no. 1, pp. 29-38, 1989.
[70] N.R. Pal and S.K. Pal, “A review on image segmentation techniques”, Pattern Recognition, vol. 26, no. 9, pp. 1277-1294, 1993.
[71] K. Rao and K. Balck, “Type classification of fingerprints: A syntactic approach”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 2, no. 3, pp. 223-231, 1980.
[72] N.K. Ratha, S. Chen and A.K. Jain, “Adaptive flow orientation-based feature extraction in fingerprint images”, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, 1995.
[73] N.K. Ratha, K. Karu, S. Chen and A.K. Jain, “A real-time matching system for large finger-print database”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 18, no. 8, pp. 799-813, 1996.
[74] A.R. Roddy, “Fingerprint features Statistical analysis and system performance estimates”, Proceedings of the IEEE, vol. 85, no. 9, pp. 1390-1420, 1997.
[75] T.N. Tan, and K.D. Baker, “Personal identification based on Pattern Recognition, vol. 33, pp. 149-160, 2000.
[76] A. Senior, “A Hidden Markov Model Fingerprint Classifier”, Proceedings of 31st Asilomar conf. on Signal, System and Computers, pp. 306-310, 1997.
[77] B.G. Sherlock, D.M. Monro and K. Millard, “Algorithm for enhancing fingerprint Electronics letters, vol. 28, no. 18, pp. 1720-1721, 1992.
[78] B.G Sherlock and D.M Monro, “A model for interpreting fingerprint topology”, Pattern Recognition, vol. 26, no. 7, pp. 1047-1095, 1993.
[79] B.G. Sherlock, D.M. Monro and K. Millard, “Fingerprint enhancement by directional Fourier filtering”, IEE Proceedings- Vision, Image and Signal Processing, no. 141, pp. 87-94, 1994.
[80] V.S. Srinivasan and N.N. Murthy, “Detection of singular points in fingerprint images”, Pattern Recognition, vol. 25, no. 2, pp. 139-153, 1992.
[81] M. Tico and P. Kuosmanen, “A multiresolution method for singular points detection in fingerprint images”, Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, vol. 4, pp. 183-186, 1999.
[82] M.R. Verma, A.K. Majumdar and B. Chatterjee, “Edge detection in fingerprints”, Pattern Recognition, vol. 20, no. 5, pp. 513-523, 1987.
[83] P. Vizcaya and L. Gerhardt, “A nonlinear orientation model for global description”, 2000.

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