低质量指纹图像预处理算法研究与应用
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摘要
随着现代科学的发展,指纹识别技术已经越来越广泛的被使用于身份识别与身份认证中。公安部门对指纹识别的应用也从传统的人工比对逐步向自动指纹识别方向发展。但是由于公安部门取像的特殊性,刑侦用的指纹往往是一些对比度较低的模糊指纹或者是大范围缺失的残破指纹,而目前在输入是低质量指纹方面的研究较少,而且仍未有一整套较为理想的预处理技术可以显著改善此类型指纹的质量。
本文以对比度较低的模糊指纹图像为研究对象,研究了该类型指纹的预处理算法。本文提出在处理低质量的指纹图像时,在通常的指纹图像预处理之前先对图像进行平滑去噪,为解决平滑后造成的边缘信息丢失的问题,本文将非线性外推增强滤波器应用于指纹图像锐化增强中,该方法在去除高频噪声的同时有效的恢复出了指纹图像损失的高频边缘信息。在进行指纹图像分割时,本文提出了一种将统计特征与感兴趣区域结合进行指纹图像分割的算法,该方法解决了感兴趣区域分割算法前景区过度分割的问题,对于低质量指纹图像分割取得良好效果。同时,通过研究分析国内外各种预处理算法,本文使用其中适合处理低质量指纹图像的一系列算法,进行图像增强。包括方向图提取、脊线频率估算、圆形Gabor滤波、二值化、细化。由于细化后的图像具有搭桥、毛刺等会导致后续的特征提取时出现伪特征,本文采用基于专家规则的方法对细化后的图像进行后处理。实验结果表明,该方法可以有效的减少特征提取时的伪特征,为后续比对打下良好基础。
由于本课题是基于刑侦用自动指纹识别系统开发的,本文除对预处理算法的研究以外,还构建了CAIFS刑侦用自动指纹识别系统的整体框架,并将上述预处理算法嵌入到CAIFS中。
本文预处理算法的实验结果均为在VC++平台下实现,结果表明,本文通过一整套预处理算法有效的改善了刑侦用低质量指纹图像的质量,为后面的比对做了先期准备。
Fingerprint recognition technology is widely used in the field of identity recognition and identification with the development of modern science. The application of fingerprint recognition in the field of criminology is now developing from manual match to automatic identification. Because of the special input environment, fingerprints used in the field of criminology are always latent. Some of them have low contrast while others are incomplete with most parts of the ridge information losing. Currently, there are few researches on low-quality fingerprint images, and there seldom has a set of pre-processing algorithms that can improve the quality of this kind of fingerprints efficiently.
The paper conducts some researches on the pre-processing algorithms based on the latent fingerprints with low contrast. Smoothing the noise before the common pre-processing step is recommended in this paper. In order to solve the problem of losing the edge information after smoothing, a nonlinear extrapolation filter is used to enhance the fingerprint images. This method recovers the losing edge information of fingerprint image. As to the segmentation part, a segmentation method combining the characteristics of statistic and the ROI region is used. The method solved the problem of over-segmentation of the foreground, and it has good effect upon the low-quality fingerprint images. By studying most pre-processing algorithms, a set of pre-processing algorithms that suitable for processing the low-quality fingerprint images is used. These algorithms include orientation extraction, estimation of ridge frequency, circular Gabor filter, binarization and thinning. To our knowledge, the burrs in the thinned image will lead to produce false characteristics. A method based on the rules is used to deal with the burrs after the analysis of the thinned images. The result shows that the method reduced the false characteristics efficiently, and benefited the matching step.
The research is based on the development of constabulary automatic fingerprint identification system, so the paper is not only researching on pre-processing algorithms but also constructing the whole structure of the CAIFS, and embedding the algorithms mentioned above.
The pre-processing algorithms are programmed under the VC++ platform, the results show that the pre-processing algorithms used in this paper can efficiently improve the quality of the latent fingerprints used by police, and give a good basis for the next matching step.
本文以对比度较低的模糊指纹图像为研究对象,研究了该类型指纹的预处理算法。本文提出在处理低质量的指纹图像时,在通常的指纹图像预处理之前先对图像进行平滑去噪,为解决平滑后造成的边缘信息丢失的问题,本文将非线性外推增强滤波器应用于指纹图像锐化增强中,该方法在去除高频噪声的同时有效的恢复出了指纹图像损失的高频边缘信息。在进行指纹图像分割时,本文提出了一种将统计特征与感兴趣区域结合进行指纹图像分割的算法,该方法解决了感兴趣区域分割算法前景区过度分割的问题,对于低质量指纹图像分割取得良好效果。同时,通过研究分析国内外各种预处理算法,本文使用其中适合处理低质量指纹图像的一系列算法,进行图像增强。包括方向图提取、脊线频率估算、圆形Gabor滤波、二值化、细化。由于细化后的图像具有搭桥、毛刺等会导致后续的特征提取时出现伪特征,本文采用基于专家规则的方法对细化后的图像进行后处理。实验结果表明,该方法可以有效的减少特征提取时的伪特征,为后续比对打下良好基础。
由于本课题是基于刑侦用自动指纹识别系统开发的,本文除对预处理算法的研究以外,还构建了CAIFS刑侦用自动指纹识别系统的整体框架,并将上述预处理算法嵌入到CAIFS中。
本文预处理算法的实验结果均为在VC++平台下实现,结果表明,本文通过一整套预处理算法有效的改善了刑侦用低质量指纹图像的质量,为后面的比对做了先期准备。
Fingerprint recognition technology is widely used in the field of identity recognition and identification with the development of modern science. The application of fingerprint recognition in the field of criminology is now developing from manual match to automatic identification. Because of the special input environment, fingerprints used in the field of criminology are always latent. Some of them have low contrast while others are incomplete with most parts of the ridge information losing. Currently, there are few researches on low-quality fingerprint images, and there seldom has a set of pre-processing algorithms that can improve the quality of this kind of fingerprints efficiently.
The paper conducts some researches on the pre-processing algorithms based on the latent fingerprints with low contrast. Smoothing the noise before the common pre-processing step is recommended in this paper. In order to solve the problem of losing the edge information after smoothing, a nonlinear extrapolation filter is used to enhance the fingerprint images. This method recovers the losing edge information of fingerprint image. As to the segmentation part, a segmentation method combining the characteristics of statistic and the ROI region is used. The method solved the problem of over-segmentation of the foreground, and it has good effect upon the low-quality fingerprint images. By studying most pre-processing algorithms, a set of pre-processing algorithms that suitable for processing the low-quality fingerprint images is used. These algorithms include orientation extraction, estimation of ridge frequency, circular Gabor filter, binarization and thinning. To our knowledge, the burrs in the thinned image will lead to produce false characteristics. A method based on the rules is used to deal with the burrs after the analysis of the thinned images. The result shows that the method reduced the false characteristics efficiently, and benefited the matching step.
The research is based on the development of constabulary automatic fingerprint identification system, so the paper is not only researching on pre-processing algorithms but also constructing the whole structure of the CAIFS, and embedding the algorithms mentioned above.
The pre-processing algorithms are programmed under the VC++ platform, the results show that the pre-processing algorithms used in this paper can efficiently improve the quality of the latent fingerprints used by police, and give a good basis for the next matching step.
引文
[1] 徐慧,基于 Gabor 滤波的指纹图像预处理算法研究,硕士学位论文,山东大学,2004
[2] 付景广,指纹识别中若干关键算法的研究,博士学位论文,中国科学院研究生院,2003
[3] 曹广忠 谢玉峰 费跃农,指纹识别技术原理及应用研究,深圳大学学报(理工版),2001,9(3):36-42
[4] Bazen A M, Gerez S H, Segmentation of Fingerprint Images, In: The 12th Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, the Netherlands, 2001 11, 276-280
[5] Chen X J, Tian J, Cheng J G et al, Segmentation of Fingerprint Images using linear classifier, EURASIP Journal on Applied Signal Processing, Brno, Czech Republic, 2004 4, 344-350
[6] 许可,指纹识别系统中低质量图像预处理技术研究,硕士学位论文,中国科学院自动化研究所,2004
[7] B.M. Mehtre, B. Chiatterjee, Segmentation of Fingerprint Images- A Composite Method, Pattern Recognition, 1989,22(4):381-385
[8] Lin Hong, Yifei Wan, Anil Jain, Fingerprint Image Enhancement Algorithm and Performance Evaluation, IEEE Trans. PAMI, 1998,20(8):777-789
[9] Areekul V, Watchareeruetai U, Tantaratana S, Fast separable Gabor Filter for Fingerprint Enhancement, In: ICBA 2004, Hong Kong, China, 2004 7, 403-409
[10] Sherlock B, Monro D, Millard K, Fingerprint Enhancement by Directional Fourier Filtering, IEE Proceedings-Vision, Image and Signal Processing, 1994, 141(2):87-89
[11] Shen L L, Kot A, Koo W M, Quality Measures of Fingerprint Images, In: AVBPA 2001, Halmstad, Sweden, 2001 6, 266-271
[12] Shi Z C, Wang Y C, Qi J, et al, A New Segmentation Algorithm for Low Quality Fingerprint Image, In: ICIG 2004, Hong Kong, China, 2004, 314-317
[13] Gu J W, Zhou J, Model-based Orientation Field Estimation for Fingerprint Recognition, In: ICIP 2003, Beijing, China, 2003 9, 899-903
[14] Yang J W, Liu L F, Jiang T Z, et al, A Modified Gabor Filter Design Method for Fingerprint Image Enhancement, Pattern Recognition, 2003, 24(12):1805-1817
[15] En Zhu, Jianping Yin, Guomin Zhang, Fingerprint Enhancement Using Circular Gabor Filter, ICIAR, 2004, 750-758
[16] Khan M A U, Khan M K, Khan M A, Fingerprint Image Enhancement Using Decimation-free Directional Filter Bank, Information Technology Journal, 2005, 4(1):16-20
[17] Xie M H, Wang Z M, Fingerprint Enhancement Based on Edge-direct Diffusion, Proceedings of the Third International Conference on Image and Graphics, HongKong, China, 2004 12, 274-277
[18] B. Moayer, K. S. Fu, A Tree System Approach for Fingerprint Pattern Recognition, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1986,8(3):376-387
[19] Anil Jain, Lin Hong, Ruud Bolle, On-Line Fingerprint Verification, IEEE Trans. PAMI, 1997,19(4):302-314
[20] A. Wahab, S.H.Chin, E.C.Tan, Novel Approach to Automated Fingerprint Recognition, IEE Proc. Vis. Image Signal Process, 1998,145(3):160-166
[21] Nalini K. Ratha, Shaoyun Chen, Anil K. Jain, Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images, Pattern Recognition, 1995,28(11) :1657-1672
[22] Marius Tico, Serban Lungu, Fingerprint Segmentation Based on Directional Information Acta Technica Napocensis-Electronics and Telecommunications, 1998,38(1):1-5
[23] Chin R T, Wan H K, Stover D L, et al, A One-pass Thinning Algorithm and its Parallel Implementation, Computer Vision Graphics Image Processing, 1987,40(1):30-40
[24] Hall R W, Optimally Small Operator Supports for Fully Parallel Thinning Algorithms, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1993,15(8):828-833
[25] 王家隆 郭成安,一种改进的图像模板细化算法,中国图像图形学报,2004,9(3):297-300
[26] Chikkerur S, Wu C, Govindaraju V., A Systematic Approach for Feature Extraction in Fingerprint Images, ICBA 2004 12, 344-350
[27] Maio D, Maltoni D, Direct Gray Scale Minutiae Detection in Fingerprints, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997,19(1):27-40
[28] Ratha N K, Karu K, Chen S, et al. A Real-time Matching System for Large Fingerprint Databases, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1996,18(8):799-813
[29] Ton J, Jain A K, Registering Landsat Images by Point Matching, IEEE Trans. On Geoscience and Remote Sensing, 1989,27(5):642-651
[30] 田捷 陈新建 张阳阳等,指纹识别技术的新进展,自然科学进展,2006 4,16(4):400-409
[31] Hayit Greenspan, Charles H. Anderson, Sofia Akber, Image Enhancement by Nonlinear Extrapolation in Frequency Space, IEEE Trans. on Image Processing, 2000 6, 9(6):1035-1049
[32] 戴天荣 张立明 王建军, 基于低通和非线性滤波的 MR 图像增强算法,中国医学物理学杂志,2003,20(3):146-150
[33] 戴天荣 张立明,一种改进的非线性外推增强图像增强算法及在高分辨率图像重建中的应用,红外与毫米波学报,2003,22(3):197-102
[34] David A., Rojas V., Jorge L., An Improved Method for Segmentation of Fingerprint Images, IEEE, 2006, CERMA’06
[35] Wang Sen, Zhang Weiwei, Wang Yangsheng, New Features Extraction and Application in Fingerprint Segmentation, ACTA Automatica Sinica, July 2003,29(4):622-629
[36] 刘强,指纹图像的高效分级分割算法,计算机应用,2003,23(1):81-83
[37] 甘树坤 欧宗瑛 魏鸿磊,基于灰度特性的指纹图像分割算法,吉林化工学院学报,2006,23(1):69-70
[38] 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(自然科学),2003,39(4):478-479
[39] 袁燕玲,基于脊线对准的指纹自动识别新方法的研究,硕士学位论文,武汉大学,2004
[40] Zhixin Shi, Venu Govindaraju, Fingerprint Image Enhancement Based on Skin Profile Approximation, IEEE, 2006, The 18th International Conference on Pattern Recognition (ICPR’06)
[41] 程建刚,指纹图像分析及其自动识别,博士学位论文,中国科学院自动化研究所,2004
[42] Jainguo Zhang, Tieniu Tan, Li Ma, Invariant Texture Segmentation Via Circular Gabor Filters, IEEE, 2002, 901-903
[43] 柏培权,基于 Gabor 滤波的自动指纹认证系统研究,硕士学位论文,苏州大学,2004
[44] Malini K., Patha Shaoyon Chen, Adapitive Flow Orientation Based Feature Extraction in Fingerprint Images, Pattern Recognition, 1885,28(11)
[45] Xiping Luo, Jie Tian, Knowledge Based Fingerprint Image Enhancement, IEEE, 2000, 782-788
[46] 刘文星 王肇圻 母国光,细化指纹上岛屿的判别和滤除,光电子激光,2002,13(3):282-286
[47] 王建永 郭成安,一种基于局部结构信息的指纹伪特征滤除算法,中国图像图形学报,2003, 8(12):1467-1474
[48] 陈沛华 陈晓光,一种修复细化指纹图像中断裂线条的新方法,通信学报,2004,25(6):115-119
[49] 胡瑢华 刘国平 余冰,模糊指纹图像的特征提取,南昌大学学报(工科版),2002,24(4):39-41
[50] Jian L, Intelligent biometric techniques in fingerprint and face recognition [M]. Boca Raton: CRC Press, 1999
[51] 王崇文 王伟 王廷才等,指纹图像后处理,计算机工程与设计,2002,23(9):35-39
[52] 巩冰,自动指纹识别系统的研究与设计,硕士学位论文,哈尔滨工程大学,2003
[2] 付景广,指纹识别中若干关键算法的研究,博士学位论文,中国科学院研究生院,2003
[3] 曹广忠 谢玉峰 费跃农,指纹识别技术原理及应用研究,深圳大学学报(理工版),2001,9(3):36-42
[4] Bazen A M, Gerez S H, Segmentation of Fingerprint Images, In: The 12th Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, the Netherlands, 2001 11, 276-280
[5] Chen X J, Tian J, Cheng J G et al, Segmentation of Fingerprint Images using linear classifier, EURASIP Journal on Applied Signal Processing, Brno, Czech Republic, 2004 4, 344-350
[6] 许可,指纹识别系统中低质量图像预处理技术研究,硕士学位论文,中国科学院自动化研究所,2004
[7] B.M. Mehtre, B. Chiatterjee, Segmentation of Fingerprint Images- A Composite Method, Pattern Recognition, 1989,22(4):381-385
[8] Lin Hong, Yifei Wan, Anil Jain, Fingerprint Image Enhancement Algorithm and Performance Evaluation, IEEE Trans. PAMI, 1998,20(8):777-789
[9] Areekul V, Watchareeruetai U, Tantaratana S, Fast separable Gabor Filter for Fingerprint Enhancement, In: ICBA 2004, Hong Kong, China, 2004 7, 403-409
[10] Sherlock B, Monro D, Millard K, Fingerprint Enhancement by Directional Fourier Filtering, IEE Proceedings-Vision, Image and Signal Processing, 1994, 141(2):87-89
[11] Shen L L, Kot A, Koo W M, Quality Measures of Fingerprint Images, In: AVBPA 2001, Halmstad, Sweden, 2001 6, 266-271
[12] Shi Z C, Wang Y C, Qi J, et al, A New Segmentation Algorithm for Low Quality Fingerprint Image, In: ICIG 2004, Hong Kong, China, 2004, 314-317
[13] Gu J W, Zhou J, Model-based Orientation Field Estimation for Fingerprint Recognition, In: ICIP 2003, Beijing, China, 2003 9, 899-903
[14] Yang J W, Liu L F, Jiang T Z, et al, A Modified Gabor Filter Design Method for Fingerprint Image Enhancement, Pattern Recognition, 2003, 24(12):1805-1817
[15] En Zhu, Jianping Yin, Guomin Zhang, Fingerprint Enhancement Using Circular Gabor Filter, ICIAR, 2004, 750-758
[16] Khan M A U, Khan M K, Khan M A, Fingerprint Image Enhancement Using Decimation-free Directional Filter Bank, Information Technology Journal, 2005, 4(1):16-20
[17] Xie M H, Wang Z M, Fingerprint Enhancement Based on Edge-direct Diffusion, Proceedings of the Third International Conference on Image and Graphics, HongKong, China, 2004 12, 274-277
[18] B. Moayer, K. S. Fu, A Tree System Approach for Fingerprint Pattern Recognition, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1986,8(3):376-387
[19] Anil Jain, Lin Hong, Ruud Bolle, On-Line Fingerprint Verification, IEEE Trans. PAMI, 1997,19(4):302-314
[20] A. Wahab, S.H.Chin, E.C.Tan, Novel Approach to Automated Fingerprint Recognition, IEE Proc. Vis. Image Signal Process, 1998,145(3):160-166
[21] Nalini K. Ratha, Shaoyun Chen, Anil K. Jain, Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images, Pattern Recognition, 1995,28(11) :1657-1672
[22] Marius Tico, Serban Lungu, Fingerprint Segmentation Based on Directional Information Acta Technica Napocensis-Electronics and Telecommunications, 1998,38(1):1-5
[23] Chin R T, Wan H K, Stover D L, et al, A One-pass Thinning Algorithm and its Parallel Implementation, Computer Vision Graphics Image Processing, 1987,40(1):30-40
[24] Hall R W, Optimally Small Operator Supports for Fully Parallel Thinning Algorithms, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1993,15(8):828-833
[25] 王家隆 郭成安,一种改进的图像模板细化算法,中国图像图形学报,2004,9(3):297-300
[26] Chikkerur S, Wu C, Govindaraju V., A Systematic Approach for Feature Extraction in Fingerprint Images, ICBA 2004 12, 344-350
[27] Maio D, Maltoni D, Direct Gray Scale Minutiae Detection in Fingerprints, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997,19(1):27-40
[28] Ratha N K, Karu K, Chen S, et al. A Real-time Matching System for Large Fingerprint Databases, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1996,18(8):799-813
[29] Ton J, Jain A K, Registering Landsat Images by Point Matching, IEEE Trans. On Geoscience and Remote Sensing, 1989,27(5):642-651
[30] 田捷 陈新建 张阳阳等,指纹识别技术的新进展,自然科学进展,2006 4,16(4):400-409
[31] Hayit Greenspan, Charles H. Anderson, Sofia Akber, Image Enhancement by Nonlinear Extrapolation in Frequency Space, IEEE Trans. on Image Processing, 2000 6, 9(6):1035-1049
[32] 戴天荣 张立明 王建军, 基于低通和非线性滤波的 MR 图像增强算法,中国医学物理学杂志,2003,20(3):146-150
[33] 戴天荣 张立明,一种改进的非线性外推增强图像增强算法及在高分辨率图像重建中的应用,红外与毫米波学报,2003,22(3):197-102
[34] David A., Rojas V., Jorge L., An Improved Method for Segmentation of Fingerprint Images, IEEE, 2006, CERMA’06
[35] Wang Sen, Zhang Weiwei, Wang Yangsheng, New Features Extraction and Application in Fingerprint Segmentation, ACTA Automatica Sinica, July 2003,29(4):622-629
[36] 刘强,指纹图像的高效分级分割算法,计算机应用,2003,23(1):81-83
[37] 甘树坤 欧宗瑛 魏鸿磊,基于灰度特性的指纹图像分割算法,吉林化工学院学报,2006,23(1):69-70
[38] 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(自然科学),2003,39(4):478-479
[39] 袁燕玲,基于脊线对准的指纹自动识别新方法的研究,硕士学位论文,武汉大学,2004
[40] Zhixin Shi, Venu Govindaraju, Fingerprint Image Enhancement Based on Skin Profile Approximation, IEEE, 2006, The 18th International Conference on Pattern Recognition (ICPR’06)
[41] 程建刚,指纹图像分析及其自动识别,博士学位论文,中国科学院自动化研究所,2004
[42] Jainguo Zhang, Tieniu Tan, Li Ma, Invariant Texture Segmentation Via Circular Gabor Filters, IEEE, 2002, 901-903
[43] 柏培权,基于 Gabor 滤波的自动指纹认证系统研究,硕士学位论文,苏州大学,2004
[44] Malini K., Patha Shaoyon Chen, Adapitive Flow Orientation Based Feature Extraction in Fingerprint Images, Pattern Recognition, 1885,28(11)
[45] Xiping Luo, Jie Tian, Knowledge Based Fingerprint Image Enhancement, IEEE, 2000, 782-788
[46] 刘文星 王肇圻 母国光,细化指纹上岛屿的判别和滤除,光电子激光,2002,13(3):282-286
[47] 王建永 郭成安,一种基于局部结构信息的指纹伪特征滤除算法,中国图像图形学报,2003, 8(12):1467-1474
[48] 陈沛华 陈晓光,一种修复细化指纹图像中断裂线条的新方法,通信学报,2004,25(6):115-119
[49] 胡瑢华 刘国平 余冰,模糊指纹图像的特征提取,南昌大学学报(工科版),2002,24(4):39-41
[50] Jian L, Intelligent biometric techniques in fingerprint and face recognition [M]. Boca Raton: CRC Press, 1999
[51] 王崇文 王伟 王廷才等,指纹图像后处理,计算机工程与设计,2002,23(9):35-39
[52] 巩冰,自动指纹识别系统的研究与设计,硕士学位论文,哈尔滨工程大学,2003