自动指纹识别技术研究
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摘要
随着社会和经济的发展,人们对身份鉴别的准确性、安全性与实用性提出了更高的要求。基于信物或口令的传统身份鉴别方式存在容易丢失、遗忘、被复制及盗用的隐患。通过辨识人的生理和行为特征进行身份认证的生物识别技术提供了一个方便可靠的解决方案。生物识别技术以生物特征为基础,以信息处理技术为手段,将生物技术和信息技术有机结合在一起。在众多的生物识别技术中,指纹识别技术以方便易用、高准确率和低成本等诸多优势备受关注,已经成为身份认证的最有效手段,在电子商务、犯罪识别、信息安全等领域得到广泛的应用。
本文在收集和分析近年来国内外有关自动指纹识别技术研究成果的基础上,对自动指纹识别系统的关键技术进行了研究。主要工作有以下几个方面:
(1)指纹图像的分割。低质量指纹图像处理是近年来自动指纹识别技术的研究重点,对低质量指纹图像的分割是实现后续处理的前提。文中在分析了方差作为分割指标的局限性基础上,从指纹图像的纹理特征出发,研究了指纹图像的灰度分布规律,提出了基于纹理的指纹图像分割算法。实验结果表明,相比于基于灰度方差的指纹图像分割算法,文中算法的分割效果更好,对噪声的抵抗能力更强。
(2)指纹特征点的提取。本文研究了两种常用的8-邻域交叉数算法,提取指纹中所有的端节点和分支点,并在此基础上提出了一种新的8-邻域交叉数算法,经过实验证明此算法能够提高特征点提取的准确度。
(3)深入阐述了指纹方向图求取方法,指纹图像细化方法等指纹图像预处理技术,具体实现了用差分比较法、统计离差法、梯度法求取指纹方向图,用快速细化算法、改进的OPTA算法、基于4连通并行细化算法的串行算法细化指纹图像。
(4)深入研究了基于三角形相似原理的点模式匹配算法和基于脊线校准的指纹匹配算法,并对两种方法的特点和优势进行了深入阐述,对比分析了各自的优、缺点,提出了指纹匹配技术后续的重点研究内容。
With the development of the society and economy, the accuracy, security and practice of the personal identification are highly required. Due to the hidden-troubles of being lost, forgotten , copied and stolen easily of traditional personal identification of token or password, biometrics that does the identify authentication by identifying the personal physiological and behavior traits provides a convinent and reliable solution . Based on the ecological traits, with a method of information-processing technology, biometrics combines the ecological technology with information technology organically. Among the numerous biometrics, more attentions have been paid to the fingerprint identification technology due to its convinence, high accuracy and low cost. The fingerprint identification, mainly used in electronic commerce, criminal identification, information security and etc, has become the most effective method for personal identification.
Based on the achievements of Automatic Fingerprint Identification Technology selected and analysed at home and abroad in recent years, this paper aims to make studies on algorithms of AFIS. Mainly works are as following:
(1) The segmentation of fingerprint image. Low-quality fingerprint image processing is the focus of research on the automated fingerprint identification technology in recent years while the low-quality fingerprint image segmentation is the prerequisite for achieving the follow-up treatment. In the paper, based on the analysis of variance as a separate indicator of the limitations ,we sutudies the gray-scale distribution of fingerprint image, and advances the theory of fingerprint image segmentation algorithm based on the texture. The results showed that, compared to the variance based on the gray fingerprint image segmentation algorithm, the algorithm of the division in the essay is much better, and the noise of the resistanceis much stronger.
(2) The extraction of fingerprint feature. This paper studied two commonly used 8 - Cross neighborhood of algorithms. Extracted all of the client nodes and branches from the fingerprints, and on the basis of this proposed a new 8 - Cross neighborhood of algorithms. The experiment proved that the algorithm can improve the accuracy of extractionon feature points.
(3) Probed deeply into elaborating a way to fingerprint image thinning image pre-processing methods such as fingerprinting technology, concrete realization of the difference by comparison, statistics from the poor, gradient method to strike a fingerprint pattern, with rapid thinning algorithm to improve The OPTA algorithm, based on the 4-connected parallel thinning algorithm serial algorithm refinement fingerprint image.
(4) Probed deeply into studying the fingerprint matching algorithm based on similar principles of the triangle pattern-matching algorithms and point on the ridge line calibration,and elaborating the characteristics and advantages of the two methods. Proposed the key follow-up studies of the fingerprint matching technology through a comparative analysis of their respective advantages and disadvantages
本文在收集和分析近年来国内外有关自动指纹识别技术研究成果的基础上,对自动指纹识别系统的关键技术进行了研究。主要工作有以下几个方面:
(1)指纹图像的分割。低质量指纹图像处理是近年来自动指纹识别技术的研究重点,对低质量指纹图像的分割是实现后续处理的前提。文中在分析了方差作为分割指标的局限性基础上,从指纹图像的纹理特征出发,研究了指纹图像的灰度分布规律,提出了基于纹理的指纹图像分割算法。实验结果表明,相比于基于灰度方差的指纹图像分割算法,文中算法的分割效果更好,对噪声的抵抗能力更强。
(2)指纹特征点的提取。本文研究了两种常用的8-邻域交叉数算法,提取指纹中所有的端节点和分支点,并在此基础上提出了一种新的8-邻域交叉数算法,经过实验证明此算法能够提高特征点提取的准确度。
(3)深入阐述了指纹方向图求取方法,指纹图像细化方法等指纹图像预处理技术,具体实现了用差分比较法、统计离差法、梯度法求取指纹方向图,用快速细化算法、改进的OPTA算法、基于4连通并行细化算法的串行算法细化指纹图像。
(4)深入研究了基于三角形相似原理的点模式匹配算法和基于脊线校准的指纹匹配算法,并对两种方法的特点和优势进行了深入阐述,对比分析了各自的优、缺点,提出了指纹匹配技术后续的重点研究内容。
With the development of the society and economy, the accuracy, security and practice of the personal identification are highly required. Due to the hidden-troubles of being lost, forgotten , copied and stolen easily of traditional personal identification of token or password, biometrics that does the identify authentication by identifying the personal physiological and behavior traits provides a convinent and reliable solution . Based on the ecological traits, with a method of information-processing technology, biometrics combines the ecological technology with information technology organically. Among the numerous biometrics, more attentions have been paid to the fingerprint identification technology due to its convinence, high accuracy and low cost. The fingerprint identification, mainly used in electronic commerce, criminal identification, information security and etc, has become the most effective method for personal identification.
Based on the achievements of Automatic Fingerprint Identification Technology selected and analysed at home and abroad in recent years, this paper aims to make studies on algorithms of AFIS. Mainly works are as following:
(1) The segmentation of fingerprint image. Low-quality fingerprint image processing is the focus of research on the automated fingerprint identification technology in recent years while the low-quality fingerprint image segmentation is the prerequisite for achieving the follow-up treatment. In the paper, based on the analysis of variance as a separate indicator of the limitations ,we sutudies the gray-scale distribution of fingerprint image, and advances the theory of fingerprint image segmentation algorithm based on the texture. The results showed that, compared to the variance based on the gray fingerprint image segmentation algorithm, the algorithm of the division in the essay is much better, and the noise of the resistanceis much stronger.
(2) The extraction of fingerprint feature. This paper studied two commonly used 8 - Cross neighborhood of algorithms. Extracted all of the client nodes and branches from the fingerprints, and on the basis of this proposed a new 8 - Cross neighborhood of algorithms. The experiment proved that the algorithm can improve the accuracy of extractionon feature points.
(3) Probed deeply into elaborating a way to fingerprint image thinning image pre-processing methods such as fingerprinting technology, concrete realization of the difference by comparison, statistics from the poor, gradient method to strike a fingerprint pattern, with rapid thinning algorithm to improve The OPTA algorithm, based on the 4-connected parallel thinning algorithm serial algorithm refinement fingerprint image.
(4) Probed deeply into studying the fingerprint matching algorithm based on similar principles of the triangle pattern-matching algorithms and point on the ridge line calibration,and elaborating the characteristics and advantages of the two methods. Proposed the key follow-up studies of the fingerprint matching technology through a comparative analysis of their respective advantages and disadvantages
引文
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[2]Davies S G..Touching big brother:How biometrics technology will fuse flesh land machine.Information Technology and People,1994,7(4):60-69
[3]Miller B.Vital signs of identity.IEEE Spectrum,1994,31(2):2230-2246
[4]Newham E.The biometrics report.New York:SJB Services,1995
[5]A.K.Jain,S.Pankanti,S.Prabhakar A.Ross.Recent Advances in Fingerprint Verification Proceedings of the 3rd International Conference on Audio-and Video-Based Person Authentication(AVBPA),2001,182-191,Sweden,June6-8
[6]A.K.Jain,L.Hong,R.Bolle.On-line fingerprint verification IEEE Transactions on Pattern Analysis and Machine Intelilgence,1997,19(4):302-314
[7]Isenor D K,Zaky S G.Fingerprint Identification Using Graph Matching[J].Pattern Recognition,1986,19(2):113-122.
[8]Andrewk,Hrechak,James.A.Mchugh,Automated Fingerprint Recognition Using Structural Matching[J].Pattern Recognition,1990,23(8):893-904.
[9]Hong L,Jainak,Pankantis:ete...Fingerprint image enhancement[A].proc.first IEEE WACV[C],1996,sarasota,Fla:1661—1671
[10]Nalinik,Ratha,Kalle Karu,ect.A real-time matching system for large fingerprint database[J].IEEE Trans on Pattern Analysis and Machine intelligence,1996,18(8):799-812.
[11]Hrechak A K,Mchugh J A,Automated fingerprint recognition using structural matching[J],Pattern Recognition,1990,23(8):893-904.
[12]Andrew Senior,A Combination Fingerprint Classifier,IEEE Trans on Pattern Analysis and Machine Intelligence,2001(23)10:1165-1174.
[13]Louis Coetzee,Elizabeth C Botha,Fingerprint recognition in images,Pattern Recognition,1994,35(5):892-896
[14]Vizcaya P R,Gerhardt L A,Multi-resolution fuzzy approach for singularity of the SPIE,1997,29(5)46-56
[15]John K Schnider,Darold C W obschall,Live scan fingerprint imagery u sing high resolution c-scan ultrasonogzaphy,Security Technology,1991,88-95
[16]Inglis C,Manchanda L,etc.A robust,l.8v 250/spl mu/w direct-contact 500 dpi fingerprint sensor.Solid-States Circuits Conference,1998,Digest of Technical papers,IEEE International,1998,284-285
[17]Jeong-woo Lee,etc.A 600 dpi capacitive fingerprint sensor chip and image-synthesis technique,Solid-State Circuits,IEEE,1999,344:469-475
[18]张洪光,刘雪梅,指纹识别中的一种局部矢量匹配算法,计算机工程,2002,28(4):106-108.
[19]Xiao Qing-han,Hazam Roafat,Fingerprint Images Preprocessing:A combined statistical and structural approach[J],Pattern Recognition,1991,24(10):985-989.
[20]D Skea,I Barrodale,R Kuwahara and R Poeckert.A Control Point Matching Algorithm[J].Pattenr Recognition,1993,26(2).
[21]金升平,陈定方.指纹细节匹配的遗传算法[J].交通与计算机,2002.20(2):30-32.
[22]J.P.Marquesdesa著.模式识别—原理方法及应用.吴逸飞译.第一版.北京:清华大学出版社,2002,214
[23]黄贤武,王佳俊,仲兴荣,指纹识别的预处理组合算法,计算机应用,2002,22(10)29-32
[24]Mehre B M,Chatterjee B.Segmentation of fingerprint image—a composition method[J].Pattern Recognition,1989,22(4):381-385.
[25]Nalini K Ratha,ShaoYun Chen,Anil K Jain.Adaptive Orientation-Based Feature Extraction in Fingerprint Images.Pattern Recognition.1995,28(11):1657—1672.
[26]王鑫 硕士论文“指纹识别算法研究及其实现”南京理工大学2004.7
[27]罗剑 硕士论文“指纹图像特征点提取和匹配算法的研究”上海大学2002.1
[28]Hong L,Wan Y,A.K.Jain.Fingerprint Image Enhancement:Algorithm and Performance Evaluaiton.IEEE on Pattern Analysis and Machine Intelligence,1998,20(8):777—789
[29]陈建新,田捷,何余良.基于频域滤波的低质量指纹增强算法.第四届生物特征识别研讨会论文集,北京,2003,12
[30]X.Luo,J.Tian.Knowledge based Fingerprint Image Enhancement.Proceedings of International Conference on Pattern Recognition,2000,4:783-786
[31]黄贤武,苏鹏程,柏培权.基于方向滤波分割的指纹自动识别系统算法.中国图象图形学报,2002,7A(8):829-834
[32]Andrew p.Witkin.Scale-space Filtering.Proceedings of the 8th International Joint Conference on Artificial Intelligence,1983,2:1019-1022
[33]L.Ma,Y.Wang,T.Tan,Iris Recogntion Based on Multichannel Gabor Filtering,In:Proceedings of the Fifth Asian Conference on Computer Vision(ACCV).Australia:Melbourne,2002,279-283
[34]B.M.Mehtre and B.Chatterjee.Segmentation of fingerprint images-acomposite method.Pattern Recognition.1989(4):381-385
[35]A.Ross,A.K Jain and J.Z.Qian.Information fusion in biometrics.In:Proc.3th International Conference on Audio and Video Based Biometric Person uthentication.Sweden:Stockholm,2002,354-360
[36]Kalle Karu,Anil K.Jain.Fingerprint Classification.Pattern Recognition,1996,29(12),389
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