基于细节特征的自动指纹识别系统的研究
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摘要
近几年，指纹识别技术获得了较大的发展，但仍是目前研究的热点，主要集中在如何提高指纹识别系统的准确率和识别速度，因此无论是在理论上还是在应用上研究自动指纹识别技术都具有非常重要的意义。
在自动指纹识别系统中，提取到的指纹特征的质量对指纹特征匹配有很大的影响。对于高质量的特征，目前的算法都能得到一个很好的匹配结果。此外，在指纹识别系统中，指纹特征的提取耗时最多。所以对指纹特征提取的研究是当前和今后指纹识别研究的重点。本文通过对指纹特征提取全过程的系统分析，对其中指纹图像的分割、纹线方向计算、纹线周期的计算、图像的增强、细节特征提取和特征匹配提出了一些新的见解。主要包括：改进了基于梯度的方向计算方法；提出了基于特征融合的指纹图像分割算法；提出了基于一维傅立叶变换的纹线周期提取算法；提出了新的Gabor滤波函数参数的设计方法，提高了滤波器的自适应性；在细节特征的提取过程中，设计了基于方向和纹线波形的二值化方法，引入了新的细节特征滤除算法，避免毛刺消除和断线连接步骤带来的时间开销，提高了细节特征的质量；在指纹特征匹配部分，设计基于统计的平移旋转参数的确定方法，克服了目前基于细节特征匹配方法的缺点。分析研究表明，新的指纹图像预处理算法从整体上提高了系统的处理效果，降低了时间复杂度；基于统计的细节特征匹配算法提高了匹配算法的鲁棒性，降低了错误拒绝的概率。
In recent years, there is a great progress in fingerprint identificationtechnology, but it still is a hot topic at present, which is focused on how toimprove the accuracy and recognition speed of the automatic fingerprintidentification system. Therefore, it's very important to research the automaticfingerprint identification technology both in theory and in application.
In the automatic fingerprint identification system, the fingerprintmatching relies on the quality of the features heavily. When the quality of thefeatures is very well, all the algorithms can get a good result. In addition,fingerprint feature extraction process is the most time-consuming in theautomatic fingerprint identification system. So the fingerprint featureextraction is the current and future key problem in fingerprint identificationtechnology. By analysis of the fingerprint feature extraction process whichincludes the fingerprint image segmentation, computing ridge direction,computing ridge period, image enhancement, minutiae extraction and detailsof the matching, some new views are proposed, which mainly include:improving orientation extraction algorithm based on the gradient, based onthe integration of the fingerprint image segmentation algorithm, based on theone-dimensional Fourier Transform ridge period computing algorithm, a newway of Gabor filter function designed which improves the adaptive capacityof the filter. In the minutiae extraction process, based on the direction andridge waveform values of the binary method is designed, a new featuredetails filtering algorithm is introduced which can retrench the resuming timeof eliminating burr and improve the reliability of the details. In thefingerprint matching process, A statistic model is proposed, which cancompute the move-rotation parameters not from the local of fingerprint butfrom the whole fingerprint, overcome the shortcomings of the presentminutiae-matching method. Analysis and some experiments show that thesenew algorithms overcome some shortcomings of present algorithms, improve the performance of the Automatic Fingerprint Identification System. Thematching algorithm based on the statistical enhances the robustness, reducesthe error probability of rejection.
在自动指纹识别系统中，提取到的指纹特征的质量对指纹特征匹配有很大的影响。对于高质量的特征，目前的算法都能得到一个很好的匹配结果。此外，在指纹识别系统中，指纹特征的提取耗时最多。所以对指纹特征提取的研究是当前和今后指纹识别研究的重点。本文通过对指纹特征提取全过程的系统分析，对其中指纹图像的分割、纹线方向计算、纹线周期的计算、图像的增强、细节特征提取和特征匹配提出了一些新的见解。主要包括：改进了基于梯度的方向计算方法；提出了基于特征融合的指纹图像分割算法；提出了基于一维傅立叶变换的纹线周期提取算法；提出了新的Gabor滤波函数参数的设计方法，提高了滤波器的自适应性；在细节特征的提取过程中，设计了基于方向和纹线波形的二值化方法，引入了新的细节特征滤除算法，避免毛刺消除和断线连接步骤带来的时间开销，提高了细节特征的质量；在指纹特征匹配部分，设计基于统计的平移旋转参数的确定方法，克服了目前基于细节特征匹配方法的缺点。分析研究表明，新的指纹图像预处理算法从整体上提高了系统的处理效果，降低了时间复杂度；基于统计的细节特征匹配算法提高了匹配算法的鲁棒性，降低了错误拒绝的概率。
In recent years, there is a great progress in fingerprint identificationtechnology, but it still is a hot topic at present, which is focused on how toimprove the accuracy and recognition speed of the automatic fingerprintidentification system. Therefore, it's very important to research the automaticfingerprint identification technology both in theory and in application.
In the automatic fingerprint identification system, the fingerprintmatching relies on the quality of the features heavily. When the quality of thefeatures is very well, all the algorithms can get a good result. In addition,fingerprint feature extraction process is the most time-consuming in theautomatic fingerprint identification system. So the fingerprint featureextraction is the current and future key problem in fingerprint identificationtechnology. By analysis of the fingerprint feature extraction process whichincludes the fingerprint image segmentation, computing ridge direction,computing ridge period, image enhancement, minutiae extraction and detailsof the matching, some new views are proposed, which mainly include:improving orientation extraction algorithm based on the gradient, based onthe integration of the fingerprint image segmentation algorithm, based on theone-dimensional Fourier Transform ridge period computing algorithm, a newway of Gabor filter function designed which improves the adaptive capacityof the filter. In the minutiae extraction process, based on the direction andridge waveform values of the binary method is designed, a new featuredetails filtering algorithm is introduced which can retrench the resuming timeof eliminating burr and improve the reliability of the details. In thefingerprint matching process, A statistic model is proposed, which cancompute the move-rotation parameters not from the local of fingerprint butfrom the whole fingerprint, overcome the shortcomings of the presentminutiae-matching method. Analysis and some experiments show that thesenew algorithms overcome some shortcomings of present algorithms, improve the performance of the Automatic Fingerprint Identification System. Thematching algorithm based on the statistical enhances the robustness, reducesthe error probability of rejection.
引文
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[2] Jain A. K, Hong L, Pankanti S. Biometrics Promising Frontiers for Emerging Identification Market. Communication ACM, 2000, 2(1):91～98
[3] 刘持平．中国原始指纹画的发现与研究．公安大学学报，2001，21(1)：2～10
[4] Moenssens A. Fingerprint Techniques. London: Chilton Book Company, 1971: 1～255
[5] Henry C. Lee, R. E. Gaensslen. Advances in fingerprint technology. France: CRC Press, 1994:1～298
[6] Maltoni. D, Maio D, Jain A. K, et al. Handbook of Fingerprint Identification. 1st ed. New York: Springer-Verlag, 2003:1～348
[7] Xudong Jiang, WeiYuan Yau. Detecting the Fingerprint minutiae by adaptive tracing the gray-level ridge. Pattern Recognition, 2001, 34(5): 999～1023
[8] 任群，田捷，一种实用的指纹图像质量评侧方法．CASIA CA IT，2001，1(1)：73～78
[9] Ratha. N. K, Shaoyun Chen, Jain A.K. Adaptive Flow Orientation-Based Feature Extraction In Fingerprint Images. Pattern Recognition, 1995, 28(11): 1657～1672
[10] 罗代升，滕奇志，何小海．基于方向图的指纹纹线检出算法研究．四川大学学报，2002，39(4)：684～688
[11] 黄贤武，苏鹏程，柏培权．基于方向滤波分割的自动指纹识别系统算法．中国图像图形学报，2002，7(8)：829～832
[12] 罗希平，田捷．自动指纹识别中的图像增强与细节匹配．软件学报，2002，13(5)：946～956
[13] LinHong, Yifei Wan, Jain A. K. Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1998, 20(8): 777～789
[14] Bazen A. M, Gerez S. H. Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 905～919
[15] 詹小四，宁新宝，尹义龙，等．多级分块尺寸下的指纹方向信息提取算法．南京大学学报，2003，39(4)：2027～2042
[16] Greenberg S, Aladjem M, Kogan. Fingerprint Image Enhancemem using Filtering Techniques. Real-Time Imaging, 2002, 8(3): 227～236
[17] Marius T. A topographic Method for Fingerprint Segmentation. the IEEE International Conference on Image Processing, 1999, 1 (1): 36～40
[18] Fang Xudong, Yau WeiYun, Ser Wee. Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge. Pattern recoginition, 2001, 34(5): 999～1013
[19] Jain A. K., Prabhakar S, Hong L, et al. Filterbank-based Fingerprint Matching. IEEE Transactions on Image Processing, 2000, 19(5): 846～859
[20] Mehtre. B. M. Segmentation of fingerprint images using the directional image. Pattern Recognition, 1987, 20(4): 429～435
[21] Methtre. B. M, Chatterjee. B. Segmentation of Fingerprint Images A Composite Method. Pattern Recognition, 1989, 22(4): 381～385
[22] 曹祥，邓宏贵，高湘蓉．基于特征融合的指纹分割算法．信息技术，2007，(3)：32～35
[23] 杨小冬，宁新宝，谭台哲．自动指纹识别系统图像分割算法的研究．南京大学学报，2004，40(4)：424～431
[24] Maio. D, Maltoni. D. Direct Gray-Scale Minutiae Detection in Fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(1): 27～40
[25] Bazen A. M, Gerez. S. H, Segmentation of Fingerprint Images. The 12th Annual Workshop on Circuits. Systems and Signal Processing. the Netherlands, 2001, 10(1): 276～280
[26] Chen. X. J, Tian. J, Cheng. J. G.. Segmentation of Fingerprint Images Using Linear Classifier. EURASIP Journal on Applied Signal Processing, 2004, 4(1): 480～494
[27] Chikkerur S, Wu C. H, Govindaraju V. A Systematic Approach for Feature Extraction in Fingerprint Images. ICBA, 2004, 12(1): 344～350
[28] En Zhu, JianpingYin, Chunfeng Hu, et al. A Systematic Method for Fingerprint Ridge Orientation Estimation and Image Segmentation. Pattern Recognition, 2006, 39(8): 1452～1472
[29] Jinwei Gu, Jie Zhou, David Zhang. A combination model for orientation field of fingerprints. Pattern Recognition, 2004, 37(2): 543～553
[30] A.M.Bazen, S. H. Gerez. Systematic Methods for the Computation of the Directional Fields and Singular Point of Fingerprints, IEEE Transactions On Pattern Analysis and Machine Intelligence, 2002, 24(7): 905～919
[31] Lin W. C, Dubes R. C. A Review of Ridge Counting in Dermatoglyphics. Pattern Recognition, 1983, 16(2): 1～8
[32] Hung D. C. D. Enhancement and Feature Purification of Fingerprint Images. Pattern Recognion, 1993, 26(11): 1661～1671
[33] Zs. M. Kovacs-Vajna, R. Rovatti, M. Frazzoni. Fingerprint Ridge Distance Computation Methodologies. Pattern Recognion, 2000, 34(1): 69～80
[34] 回红，陈祥献，周泓，汪乐宇．Gabor函数实现基于结构的指纹识别．浙江大学学报，2004，8(6)：712～716
[35] Hong L, JainA. K, Pankanti S, et al. Fingerprint enhancement. In Proceedings of the IEEE Workshop on Applications of Computer Vision, 1996, 1 (1): 202～207
[36] Sherlock D, Monro D. M, Millard K. Fingerprint enhancement by directional Fourier filter. IEE Proceedings of Vision Image and Signal Processing, 1994, 141(2): 87～94
[37] Mehtre B. Fingerprint image analysis for automatic identification. Machine Vision and Application, 1993, 22 (6): 124～139
[38] O'Gorman L, Neckerson JV. An approach to fingerprint filter design. Pattern Recognition, 1989, 22(1): 29～38
[39] Almansa A, Lindeberg T. Fingerprint enhancement by shape adaptation of scale- space operators with automatic scale-selection. IEEE Transactions on Image Processing, 2000, 9(12): 2027～2042
[40] 付景广，许刚，王裕国．基于二值图像的指纹细节点提取．计算机研究与发展，2004，41(4)：720～727
[41] Mehtre B. M., Murthy N. N., Kapoor S. Segmentation of fingerprint images using the directional images. Pattern Recognition, 1987, 20(4): 429～435
[42] Mehtre B. M., Chatterjee B. Segmentation of fingerprint images-acomposite method. Pattern Recognition, 1995, 28(12): 1657～1672
[43] 冯星奎，李林艳，颜祖泉．一种新的指纹图像细化算法．中国图像图形学报，1994，(10)：835～838
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