自动指纹识别系统的研究与设计
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摘要
指纹具有唯一性和稳定性，因此被人们用来当作鉴别个人身份的主要依据。自动指纹识别系统是基于计算机来进行指纹识别的技术，具有方便、高效、安全、可靠等优点，在金融安全、数据加密、电子商务等各个领域都得到了广泛的应用，并将在我们的生产和生活中发挥越来越重要的作用。
本文的内容正是关于自动指纹识别系统的研究与开发，按照设计过程，本文主要包括三个大部分：指纹图像的预处理、特征提取以及匹配。
指纹图像的预处理又可以分为灰度图滤波去噪、二值化、二值化图像去噪、细化和细化后去噪五个部分。本文先基于指纹的方向图设计出方向滤波器对原图像进行滤波去噪，然后使用局部平滑阈值自适应二值化算法，将灰度图像进行二值化，并采用快速傅氏变换对所得到的二值化图像进行去噪处理。接下来使用细化模板对二值化图像进行细化，并针对细化图中各种噪声的拓扑结构将它们一一滤除。
指纹图像的特征提取主要是提取指纹的细节特征及其位置。本文先采用脊线跟踪法将指纹图中的细节特征全部找出来，再对每个细节特征进行验证，尽量去除伪特征点。然后采用求Poincare Index值的方法确定指纹的中心点，并作为参照点来确定每个特征点相对参照点的位置。
指纹图像的匹配过程包括了图像校准和细节匹配两个部分。首先，找到输入图像和模板图像的参照点对，然后将两幅图像中的细节特征点相对于各自的参照点转化为极坐标形式，最后进行比对，确定两幅图像是否来自于同一手指。
经实验证明，本文所设计的自动指纹识别系统系统是可靠、有效的。
Due to their uniqueness and persistence, fingerprints are used as main basis of personal identity. Automated fingerprint identification system, a technology of fingerprint identification using computer, is of convenience, high efficiency, security and reliability. It has been applied in many fields such as financial security, data encryption, electronical business and will play a more and more important role in our life.
This paper is about the study and design of automated fingerprint identification system. According to the process of the design, the paper can be devided into three components: pre-processing, feature extraction, matching of fingerprint images.
Fingerprint image pre-processing has five parts: filtration in gray-scale image, binarization, filtration in binary image, thinning and filtration in thinning image. In this paper, we firstly design orientation filters based on directional image of fingerprint and employ them to denoise gray-scale image. Then, we binarize the gray-scale image with local self-adaptive binarization smoothness algorithm and eliminate the noises from the binary image with fast Fourier transform algorithm. Afterwards, by using thinning templates, we get the skeleton fingerprint image from the binary image. After thinning, we get rid of the noises from the acquired skeleton image according to their configuration.
Fingerprint image feature extraction mainly extracts the minutiae and their positions. Firstly, this paper presents an algorithm based on ridge following to extract all minutiae from the pre-processed image. Secondly, we validate these minutiae and eliminate pseudo ones. Then, by computing the value of Poincare Index, we can find the core of the fingerprint. Finally, we can fix on the relative positions of the minutiae according to the core.
Fingerprint image matching has two steps: image adjustment and minutiae matching. First of all, We select a referrence point pair of the input image and the
template image. And then we transform the minutiae positions into polar coordinates. Finally, we match the input image with the template one to judge whether these two images are captured from the same finger.
Experiments have been done and the results show that the devised automated fingerprint identification system is effective and reliable.
本文的内容正是关于自动指纹识别系统的研究与开发，按照设计过程，本文主要包括三个大部分：指纹图像的预处理、特征提取以及匹配。
指纹图像的预处理又可以分为灰度图滤波去噪、二值化、二值化图像去噪、细化和细化后去噪五个部分。本文先基于指纹的方向图设计出方向滤波器对原图像进行滤波去噪，然后使用局部平滑阈值自适应二值化算法，将灰度图像进行二值化，并采用快速傅氏变换对所得到的二值化图像进行去噪处理。接下来使用细化模板对二值化图像进行细化，并针对细化图中各种噪声的拓扑结构将它们一一滤除。
指纹图像的特征提取主要是提取指纹的细节特征及其位置。本文先采用脊线跟踪法将指纹图中的细节特征全部找出来，再对每个细节特征进行验证，尽量去除伪特征点。然后采用求Poincare Index值的方法确定指纹的中心点，并作为参照点来确定每个特征点相对参照点的位置。
指纹图像的匹配过程包括了图像校准和细节匹配两个部分。首先，找到输入图像和模板图像的参照点对，然后将两幅图像中的细节特征点相对于各自的参照点转化为极坐标形式，最后进行比对，确定两幅图像是否来自于同一手指。
经实验证明，本文所设计的自动指纹识别系统系统是可靠、有效的。
Due to their uniqueness and persistence, fingerprints are used as main basis of personal identity. Automated fingerprint identification system, a technology of fingerprint identification using computer, is of convenience, high efficiency, security and reliability. It has been applied in many fields such as financial security, data encryption, electronical business and will play a more and more important role in our life.
This paper is about the study and design of automated fingerprint identification system. According to the process of the design, the paper can be devided into three components: pre-processing, feature extraction, matching of fingerprint images.
Fingerprint image pre-processing has five parts: filtration in gray-scale image, binarization, filtration in binary image, thinning and filtration in thinning image. In this paper, we firstly design orientation filters based on directional image of fingerprint and employ them to denoise gray-scale image. Then, we binarize the gray-scale image with local self-adaptive binarization smoothness algorithm and eliminate the noises from the binary image with fast Fourier transform algorithm. Afterwards, by using thinning templates, we get the skeleton fingerprint image from the binary image. After thinning, we get rid of the noises from the acquired skeleton image according to their configuration.
Fingerprint image feature extraction mainly extracts the minutiae and their positions. Firstly, this paper presents an algorithm based on ridge following to extract all minutiae from the pre-processed image. Secondly, we validate these minutiae and eliminate pseudo ones. Then, by computing the value of Poincare Index, we can find the core of the fingerprint. Finally, we can fix on the relative positions of the minutiae according to the core.
Fingerprint image matching has two steps: image adjustment and minutiae matching. First of all, We select a referrence point pair of the input image and the
template image. And then we transform the minutiae positions into polar coordinates. Finally, we match the input image with the template one to judge whether these two images are captured from the same finger.
Experiments have been done and the results show that the devised automated fingerprint identification system is effective and reliable.
引文
[1] 霍宏涛，林小竹，何薇．数字图像处理．第一版．北京理工大学出版社，2002：168-183页
[2] Bolle, S. Pankanti. Biometrics Personal Identification in Networked Society.Kluwer Academic Publishers, 1999: 3-10P
[3] H. Lee, R. Gaensslen, Eds.. Advances in Fingerprint Technology. New York: Elsevier, 1991: 1-7P
[4] Federal Bureau of Investigation. The Science of Fingerprints: Classification and Uses. Washington D.C.: GPO, 1984
[5] E. Newham. The Biometric Report. New York: SJB Services, 1995: 11-20P
[6] H. Cummins, C. Midlo. Fingerprints, Palms and Soles. New York: Dover,1961: 5-18P
[7] F. Galton. Fingerprints. London: McMilan, 1892
[8] E. Henry. Classification and uses of fingerprints. London: Routledge,1900
[9] Anil K. Jain, Lin Hong, Sharath Pankanti, Ruud Bolle. An identity-authentication system using fingerprints. Proceedings of the IEEE.1997, 85(9): 1365-1388P
[10] N. K. Ratha, S. Chen, A. K. Jain. Adaptive flow directional-based feature extraction in fingerprint images. Pattern Recognition. 1995, 28(11): 1657-1672P
[11] http://www.chinazhiwen.com
[12] Ling Hong, Anil Jain. Integrating faces and fingerprints for personal identification. IEEE Trans. on Pattern Analysis and Machine Intelligence.1998, 20(12): 1295-1307P
[13] Ahmed S Mohamed, etc. A prototype of an automated fingerprint identification system. Empreinte. 1995, 23(2): 98-116P
[14] Maio D., Maltoni D. Direct gray-scale minutiae detection in fingerprints.
[15]冯星奎，李林艳，颜祖泉．一种新的指纹图象细化算法．中国图象图形学报．1999，4(10)：835-838页
[16]Anil K Jain, Lin Hong. A multichannel approach to fingerprint classification.IEEE Trans. on Pattern Analysis and Machine Intelligence. 1999, 21(4): 348-359P
[17]陈跃峰，肖自美，王植，方艳梅．指纹图象特征提取的新方法．计算机工程与应用．2001，16：26-29页
[18]Nalini K. Ratha, Kalle Karu, Shaoyun Chen, Ani K. Jain. A real-time matching system for large fingerprint databases. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1996, 18(8): 799-813P
[19]Anil Jain, Lin Hong, Ruud Bolle. On-line fingerprint verification. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1997, 19(4): 302-313P
[20]G. Stockman, S. Kopstein, S. Benett. Matching images to models for registration and object detection via clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1982, 4(3): 229-241P
[21]Sanjay Ranade, Azriel Rosenfeld. Point pattern matching by relaxation.Pattern Recognition. 1980, 12(4): 269-275P
[22]J. R Pascual Starink, Eric Backer. Finding point correspondences using simulated annealing. Pattern Recognition. 1995, 28(2): 231-240P
[23]A. K. Hrechak, J. A. Mchugh. Automated fingerprint recognition using structural matching. Pattern Recognition. 1990, 23(8): 893-904P
[24]M. K. Sparrow, P. J. Sparrow. A topological approach to the matching of single fingerprints: development of algorithms for use on latent fingermarks. Washington D. C.: U. S. Government Printing Office. 1985: 126-134P
[25]D. K. Isenor, S. G. Zaky. Fingerprint identification using graph matching.Pattern Recognition. 1986, 19(2): 113-122P
[26]D. Skea, I. Barrodale, R. Kuwahara, R. Poeckert. A control point matching algorithm. Pattern Recognition. 1993, 26(2): 269-276P
[27]Robert S. Germain, Anderea Califano, Scott Colville. Fingerprint matching using transformation parameter clustering. IEEE Computational
Science&Engineering, 1997, October-December: 42-49P
[28]Srdjan Sobajic, Amit Manwani, George Barbastathis. CNS286 project: fingerprint recognition. Technical Report. 1997, California Institute of Technology.
[29]P. Bald, Y. Chauvin. Neural networks for fingerprint recognition. Neural Computation. 1993,5(3): 402-418P
[30]J.P. Ratkovic. Increasing efficiency in the criminal justice system: the uses of new technology for crimina indentification and latent print processing.California: The Rand Corporation, 1980, September
[31]Lin Hong, Yifei Wan, Anil Jain. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1998, 20(8): 777-789P
[32]韩伟红，黄子中，王志英．指纹自动识别系统中的预处理技术．计算机研究与发展．1997，34(12)：914-920页
[33]黄人，魏敏等．基于结构极坐标变换的指纹识别．计算机工程与应用．2000，20(6)：33-35页
[34]夏勇，田捷等．一种高效的自适应指纹图像压缩算法．计算机学报．1999，22(5)：525-528页
[35]邓志才．面向低质指纹的处理算法的研究．华南师范大学学报(自然科学版)．1997(2)：18-24页
[36]黄席樾，马笑潇等．基于遗传算法的神经网络指纹自动分类．重庆大学学报(自然科学版)．2001，24(1)：74-77页
[37]杨海军，梁德群，田原．基于方向场特征的指纹图像奇异点的检测．自动化学报．2001，27(2)：272-275页
[38]曾京文，汪庆宝，胡健．指纹自动识别中的中心点搜索和特征分块抽取方法．北京工业大学学报．1996，22(4)：115-121页
[39]洪波，荣钢．黄韬．一种基于遗传算法的指纹比对算法．清华大学学报(自然科学版)．2001，41(3)：93-96页
[40]陈勇，杨新等．基于方向场的指纹分类快速算法．模式识别与人工智能．2000，13(4)：474-477页
[41]林喜荣，于政涛等．一种利用动态滤波模板进行指纹图像增强的算法．清华大学学报(自然科学版)．2001，41(8)：37-40页
[42]杜朝晖，杨新等．基于Walsh变换检测指纹方向场的算法．上海交通大学学报．2000，34(5)：708-714页
[42]李哓昆．基于结构特征的指纹识别．计算机工程与科学．1999，21(2)：25-29页
[43]张玲，张钹等．对图形识别具有平移、旋转、伸缩不变性的神经网络．计算机学报．1998，21(2)：127-136页
[44]王向军等．指纹图像的定量化区域矢量编码与识别．光学技术．2000，26(6)：513-516页
[45]罗希平，田捷．自动指纹识别中的图像增强和细节匹配算法．软件学报．2002，13(5)：946-956页
[46]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．2002，7(8)：829-834页
[47]黄贤武，王加俊，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页
[48]吴建明，施鹏飞，周洋．一种指纹识别的细节特征匹配的方法．测控技术．2002，21(5)：19-38页
[49]罗剑，张维新．基于脊线跟踪的指纹角度特征的提取方法及应用．上海大学学报(自然科学版)．2002，8(2)：104-110页
[50]简兵，庄镇泉，李海鹰，王睿斌．基于脊线跟踪的指纹图细节提取算法．电路与系统学报．2001，6(3)：1-5页
[2] Bolle, S. Pankanti. Biometrics Personal Identification in Networked Society.Kluwer Academic Publishers, 1999: 3-10P
[3] H. Lee, R. Gaensslen, Eds.. Advances in Fingerprint Technology. New York: Elsevier, 1991: 1-7P
[4] Federal Bureau of Investigation. The Science of Fingerprints: Classification and Uses. Washington D.C.: GPO, 1984
[5] E. Newham. The Biometric Report. New York: SJB Services, 1995: 11-20P
[6] H. Cummins, C. Midlo. Fingerprints, Palms and Soles. New York: Dover,1961: 5-18P
[7] F. Galton. Fingerprints. London: McMilan, 1892
[8] E. Henry. Classification and uses of fingerprints. London: Routledge,1900
[9] Anil K. Jain, Lin Hong, Sharath Pankanti, Ruud Bolle. An identity-authentication system using fingerprints. Proceedings of the IEEE.1997, 85(9): 1365-1388P
[10] N. K. Ratha, S. Chen, A. K. Jain. Adaptive flow directional-based feature extraction in fingerprint images. Pattern Recognition. 1995, 28(11): 1657-1672P
[11] http://www.chinazhiwen.com
[12] Ling Hong, Anil Jain. Integrating faces and fingerprints for personal identification. IEEE Trans. on Pattern Analysis and Machine Intelligence.1998, 20(12): 1295-1307P
[13] Ahmed S Mohamed, etc. A prototype of an automated fingerprint identification system. Empreinte. 1995, 23(2): 98-116P
[14] Maio D., Maltoni D. Direct gray-scale minutiae detection in fingerprints.
[15]冯星奎，李林艳，颜祖泉．一种新的指纹图象细化算法．中国图象图形学报．1999，4(10)：835-838页
[16]Anil K Jain, Lin Hong. A multichannel approach to fingerprint classification.IEEE Trans. on Pattern Analysis and Machine Intelligence. 1999, 21(4): 348-359P
[17]陈跃峰，肖自美，王植，方艳梅．指纹图象特征提取的新方法．计算机工程与应用．2001，16：26-29页
[18]Nalini K. Ratha, Kalle Karu, Shaoyun Chen, Ani K. Jain. A real-time matching system for large fingerprint databases. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1996, 18(8): 799-813P
[19]Anil Jain, Lin Hong, Ruud Bolle. On-line fingerprint verification. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1997, 19(4): 302-313P
[20]G. Stockman, S. Kopstein, S. Benett. Matching images to models for registration and object detection via clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1982, 4(3): 229-241P
[21]Sanjay Ranade, Azriel Rosenfeld. Point pattern matching by relaxation.Pattern Recognition. 1980, 12(4): 269-275P
[22]J. R Pascual Starink, Eric Backer. Finding point correspondences using simulated annealing. Pattern Recognition. 1995, 28(2): 231-240P
[23]A. K. Hrechak, J. A. Mchugh. Automated fingerprint recognition using structural matching. Pattern Recognition. 1990, 23(8): 893-904P
[24]M. K. Sparrow, P. J. Sparrow. A topological approach to the matching of single fingerprints: development of algorithms for use on latent fingermarks. Washington D. C.: U. S. Government Printing Office. 1985: 126-134P
[25]D. K. Isenor, S. G. Zaky. Fingerprint identification using graph matching.Pattern Recognition. 1986, 19(2): 113-122P
[26]D. Skea, I. Barrodale, R. Kuwahara, R. Poeckert. A control point matching algorithm. Pattern Recognition. 1993, 26(2): 269-276P
[27]Robert S. Germain, Anderea Califano, Scott Colville. Fingerprint matching using transformation parameter clustering. IEEE Computational
Science&Engineering, 1997, October-December: 42-49P
[28]Srdjan Sobajic, Amit Manwani, George Barbastathis. CNS286 project: fingerprint recognition. Technical Report. 1997, California Institute of Technology.
[29]P. Bald, Y. Chauvin. Neural networks for fingerprint recognition. Neural Computation. 1993,5(3): 402-418P
[30]J.P. Ratkovic. Increasing efficiency in the criminal justice system: the uses of new technology for crimina indentification and latent print processing.California: The Rand Corporation, 1980, September
[31]Lin Hong, Yifei Wan, Anil Jain. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1998, 20(8): 777-789P
[32]韩伟红，黄子中，王志英．指纹自动识别系统中的预处理技术．计算机研究与发展．1997，34(12)：914-920页
[33]黄人，魏敏等．基于结构极坐标变换的指纹识别．计算机工程与应用．2000，20(6)：33-35页
[34]夏勇，田捷等．一种高效的自适应指纹图像压缩算法．计算机学报．1999，22(5)：525-528页
[35]邓志才．面向低质指纹的处理算法的研究．华南师范大学学报(自然科学版)．1997(2)：18-24页
[36]黄席樾，马笑潇等．基于遗传算法的神经网络指纹自动分类．重庆大学学报(自然科学版)．2001，24(1)：74-77页
[37]杨海军，梁德群，田原．基于方向场特征的指纹图像奇异点的检测．自动化学报．2001，27(2)：272-275页
[38]曾京文，汪庆宝，胡健．指纹自动识别中的中心点搜索和特征分块抽取方法．北京工业大学学报．1996，22(4)：115-121页
[39]洪波，荣钢．黄韬．一种基于遗传算法的指纹比对算法．清华大学学报(自然科学版)．2001，41(3)：93-96页
[40]陈勇，杨新等．基于方向场的指纹分类快速算法．模式识别与人工智能．2000，13(4)：474-477页
[41]林喜荣，于政涛等．一种利用动态滤波模板进行指纹图像增强的算法．清华大学学报(自然科学版)．2001，41(8)：37-40页
[42]杜朝晖，杨新等．基于Walsh变换检测指纹方向场的算法．上海交通大学学报．2000，34(5)：708-714页
[42]李哓昆．基于结构特征的指纹识别．计算机工程与科学．1999，21(2)：25-29页
[43]张玲，张钹等．对图形识别具有平移、旋转、伸缩不变性的神经网络．计算机学报．1998，21(2)：127-136页
[44]王向军等．指纹图像的定量化区域矢量编码与识别．光学技术．2000，26(6)：513-516页
[45]罗希平，田捷．自动指纹识别中的图像增强和细节匹配算法．软件学报．2002，13(5)：946-956页
[46]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．2002，7(8)：829-834页
[47]黄贤武，王加俊，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页
[48]吴建明，施鹏飞，周洋．一种指纹识别的细节特征匹配的方法．测控技术．2002，21(5)：19-38页
[49]罗剑，张维新．基于脊线跟踪的指纹角度特征的提取方法及应用．上海大学学报(自然科学版)．2002，8(2)：104-110页
[50]简兵，庄镇泉，李海鹰，王睿斌．基于脊线跟踪的指纹图细节提取算法．电路与系统学报．2001，6(3)：1-5页