面向低质量指纹的图像增强算法研究
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摘要
指纹是手指末端正面皮肤上由乳头凸起的摩擦脊线形成的花纹,具有各人各指不同、终身稳定不变的特性。指纹因其蕴涵大量的人身个体信息,而具有很高的人身识别价值。近百年来,人们通过对指纹不懈的研究和探索,逐步对指纹的特征体系有了清晰的认识,并基于此对指纹特征进行了分类,提出了指纹鉴定的科学依据和程序。
目前,指纹识别技术已经在现代生物识别技术中占有相当重要的位置。从实用性和可行性角度看,指纹识别技术能够高效、快捷、方便的自动完成指纹的纹形分类、特征提取、图像的存储、检索以及比对、细节特征匹配等一系列工作,具有方便、高效、客观、安全等诸多优点,优于其它生物识别技术,已被认为是一种理想的身份认证技术。
从20世纪60年代起,计算机技术进入指纹识别、鉴定领域,英国、美国、法国、日本等计算机发达的国家先后研制出各具特色的指纹自动识别系统,为指纹鉴定开辟了新的途径。目前,计算机指纹识别技术已经在司法、金融安全、数字加密、电子商务等各个领域得到了广泛的应用,在我们未来的生活中发挥越来越重要的作用。
近年来,由于数字图象处理学以及硬件技术的迅速发展,指纹识别技术获得相当大的进展,但仍然不能满足社会发展的需要,以指纹识别广泛代替其它识别技术(如印鉴,钥匙,密码,签字)是面向二十一世纪的具有深远意义的课题,有关指纹自动识别技术的研究已成为模式识别、图象处理以及计算机视觉等领域中极为关注的热点。
指纹识别技术通常使用指纹的一般特征来进行种类识别,在种类识别的基础上再对指纹的细节特征进行系统性的比较,然后作出是否同一的判断。它一般都由以下模块组成:指纹图像采集模块;指纹图像预处理模块;特征提取模块;特征匹配模块。其中,指纹图像预处理模块又包括:图像质量评估,图像分割、图像增强、细化、二值化等步骤。
指纹识别技术中,图像增强技术是其中一个非常重要的步骤。如果指纹图像得不到准确、显著的增强,指纹特征就难以被准确提取。许多学者对指纹图像增强方法进行了探讨,其中,Coetzee等使用Marr-Hildreth边缘算子得到指纹灰度图的脊边缘图,提出了采用卷积模板来进行增强的方法,Randolph等提出了一种使用方向滤波器组来对二值图像来进行增强的方法,Sherlock等提出了采用傅立叶滤波器来增强指纹图像的方法,Hang提出了使用Gabor滤波器的方法来增强指纹图像的方法。事实证明,这些方法用于金融安全、数字加密、电子商务等安保领域都取得了较好的效果。
但是,这些方法均为通过对指纹图像进行局域分析来估计指纹方向图,而后对图像进行滤波增强。当系统中输入的指纹源图像质量较低时,其分辨率会大大降低,这些方法难以准确得到局域的方向图估计值,将无法进行有效的滤波增强。实质上,司法领域中所需识别的指纹图像大多遭到严重污染,分辨率较低,因此,现有的图像增强算法大多难以在该领域发挥大的作用。
目前,现有的指纹识别技术在处理低质量指纹方面的效能明显较低,难以满足鉴定的需求。这主要是因为,现有指纹识别技术的最关键环节——图像增强技术大多是针对较高质量的指纹图像而设计的,对严重污染分辨率低的低质量指纹图像难以达到理想的增强效果。
针对此情况,为增强指纹识别系统处理低质量指纹的能力,在总结相关研究成果的基础上,本文提出了一套新的指纹图像增强算法。该套算法分为三部分:一是纹理信息放大算法。此算法通过改进局部直方图均衡化算法的累积分布函数而得,其中包含一个非线性的指纹纹理信息放大器,用于放大分辨率低的指纹纹理信息,增强纹线对比度,有助于提高鉴定人员对模糊指纹判别的准确性;二是指纹方向图二步算法。此算法通过改进经典方向图算法而得,可基于指纹纹理放大图获取方向图。此算法在计算脊点方向后再计算谷点方向,因此具备一定的抗噪性能。而且,此算法引入直方图法计算谷点方向,对方向图的平滑滤波进一步提高了方向图的准确性;三是方向图修正算法,此算法结合指纹纹线特征,通改进经典的块方向图算法而得,可在更大的邻域窗口中计算块方向图。与传统块方向图算法相比,此算法的处理窗口可比其大3至5倍。当指纹图像出现大面积污染时,由于大邻域窗口中信息量大,采用此算法仍有较大可能得出准确的方向图,据此进行滤波,就能得到更加清晰、准确的指纹图像。这些算法修正方向图的能力有了较大的提高。
大量实验表明,对于指纹图像中块面积大于7×7个象素的低质量区域,现有的经典指纹图像增强算法是难以进行理想的增强和修复的。本文所述纹理放大算法可修复7×7个象素以下大小的低质量块,二步方向算法可修复9×9个象素以下大小的低质量块,方向修正算法则可修复13×13个象素以下大小的低质量块,相对传统算法而言,其图像增强效能明显提高。由此可见,本文提出的方法具有更好的增强效果,在处理低质量指纹图像时,显示出明显的优越性。
本文所述算法虽能够在较大局部窗口内修正方向图,但依然不具备从指纹宏观整体入手修正方向图的能力。因此,对于更大面积的低质量块,本文所述算法仍不能给予理想的修复和增强,需要进一步结合指纹自身的特点,不断扩展算法的处理窗口,使之具备从宏观上修正点方向图的能力。
Fingerprint is a kind of flower patterns formed by mastoid ridge lines that grow on the cutis of finger tail end, which have the characteristics that a piece finger of each one is different and settling invariability for life. Fingerprint has great value for person identification. In the near century, after investigating and exploring unceasingly, people have clear cognition on the characteristic system of fingerprint, classify the fingerprint characteristics and put forward the scientific fingerprint identification thereunder and procedure.
At present, fingerprint identification technology (FIT) takes up important station in modern biology identification technologies. Looking from the practicability and the feasibility, FIT can automatically accomplish a series of works, such as fingerprint classification, character distilling, images memory, search, contrast and matching efficiently, swiftly and conveniently. FIT is of the advantages of convenience, high efficiency, impersonality and security, which is more excellent than identification technologies and has been considered as a kind of perfect identification technology.
From the 1960's, computer technology enters into fingerprint identification sight. The countries which have developed computer technology, such as the UK, USA, France and Japan, develop various FIT successively and opened up new ways for fingerprint identification. At present, FIT has been applied widely in financial security, digital encryption; electronic business and judicial practice, which will play more and more important effect in future.
In recent years, with the rapid development of the digital image-processing and the technology of hardware, FIT has attained largish development. But it also can not meet the need of society development. In the 21st, replacing other identification technologies (such as seal, key, code and signature) by FIT far-rangingly will become a significant topic. The research of FIT has become a focus among the field of model recognition, manipulating an image and computer vision.
Usually, FIT uses general characters of fingerprints to carry through species identification. Basing on it, FIT will systemically compare the detail Characters of fingerprints, and then judge that the fingerprints is whether identical or not. FIT include the modules following: Fingerprint image collection, image pretreatment, character pick-up and character matching. Image pretreatment include the steps following: image quality evaluation, image segmentation, image enhancement, fragmentation, binarization and so on.
In the identification technology, image enhancement technology is a all-important one. If fingerprint image is not enhanced effectively, fingerprint's features can not be extracted. Many scholars have discussed fingerprint image enhancement modes. Coetzee etc use Marr-Hildreth edge operator to gain the ridge margin image of fingerprint grey chat and bring forward a method that adopts convolution template to enhance the image. Randolph etc bring forward a method that adopts a set of directional filters to enhance the binary image. Sherlock etc bring forward a method that adopts Fourier filter to enhance the image. Hang etc bring forward a method that adopts Gabor filter to enhance the image. Proof by facts, used in finance security, digital encrypts, electronic business fields, these methods can gain preferable effect.
However, these methods estimate fingerprint directed graph by analyzing fingerprint image locally, and then enhance the image by filtering. When the source fingerprint image quality is inferior whose resolving power reduces greatly, these methods can't gain local directed graph well and truly and put up effective filtering enhancement. Virtually, fingerprint images which are polluted badly just have low resolution in judicature field. Therefore, the most algorithms at present are difficult to exert effect.
At present, in the aspect of dealing with low quality fingerprint, the efficacy of existing FIT is obviously inferior and difficult to satisfy the need of identification. The primary reason is that the most pivotal tache of existing FIT——image enhancement technologies are devised for high quality fingerprint images, which can not gain perfect effect at enhancing lower quality fingerprint images.
Aiming at this complexion, in order to advance the efficiency of FIT for dealing with low quality images, this article puts forward a suit of new fingerprint image enhancement algorithms. The algorithms have three portions: The first one is texture information amplification algorithm that is gained by ameliorating the cumulative distribution function of local area histogram equalization algorithm and includes a non-linear fingerprint texture information amplifier, can be used to the faint texture information of the low quality fingerprints, enhance texture contrast; The second one is fingerprint directed graph two steps algorithm. It can be gained by ameliorate classics directed graph algorithm and calculate directed graph with texture amplification image. It calculates valley points' direction after calculating ridge points' direction and has the capability of clearing up noise at a certain extent. Moreover, it adopts histogram arithmetic to calculate valley points' direction and carries through smoothing filtering for directed graph, which can improve the veracity of directed graph ulteriorly. The third one is directed graph correction algorithm. It is gained by ameliorate classics piece directed graph algorithms and can calculate piece directed graph in bigger neighborhood. Compared with the traditional directed graph algorithms, its calculation window is 3 to 5 times bigger than them. When the image is polluted in a large scale, it is more possible to work out exact directed graph by this arithmetic than traditional algorithms and gain more clear-cut, exact fingerprint image by filtering. These algorithms' capacity for repairing directed graph has biggish improvement.
A mass of experiments indicate that, it is difficult for existing classical fingerprint image enhancement algorithms to enhance and repair the low quality blocks whose area is bigger than 7×7 pixels, the texture amplification algorithm can repair the blocks whose area is less than 7×7 pixels, the directed graph two steps algorithm can repair the blocks whose area is less than 9×9 pixels, the directed graph correction algorithm can repair the blocks whose area is less than 13×13 pixels perfectly. Compared with traditional algorithms, the image enhancement efficiency of these algorithms is advanced evidently. The algorithms put forward in this article have better effect and reveal obvious advantages when dealing with low quality fingerprint images.
Although the algorithms put forward in this article can repair directed graph in a larger local window, they still can not do that macroscopically. Therefore, these algorithms still can not repair and enhance the low quality blocks whose area is bigger than 13×13 pixels perfectly, need to expand the algorithm's treatment window based on fingerprint's characteristics.
目前,指纹识别技术已经在现代生物识别技术中占有相当重要的位置。从实用性和可行性角度看,指纹识别技术能够高效、快捷、方便的自动完成指纹的纹形分类、特征提取、图像的存储、检索以及比对、细节特征匹配等一系列工作,具有方便、高效、客观、安全等诸多优点,优于其它生物识别技术,已被认为是一种理想的身份认证技术。
从20世纪60年代起,计算机技术进入指纹识别、鉴定领域,英国、美国、法国、日本等计算机发达的国家先后研制出各具特色的指纹自动识别系统,为指纹鉴定开辟了新的途径。目前,计算机指纹识别技术已经在司法、金融安全、数字加密、电子商务等各个领域得到了广泛的应用,在我们未来的生活中发挥越来越重要的作用。
近年来,由于数字图象处理学以及硬件技术的迅速发展,指纹识别技术获得相当大的进展,但仍然不能满足社会发展的需要,以指纹识别广泛代替其它识别技术(如印鉴,钥匙,密码,签字)是面向二十一世纪的具有深远意义的课题,有关指纹自动识别技术的研究已成为模式识别、图象处理以及计算机视觉等领域中极为关注的热点。
指纹识别技术通常使用指纹的一般特征来进行种类识别,在种类识别的基础上再对指纹的细节特征进行系统性的比较,然后作出是否同一的判断。它一般都由以下模块组成:指纹图像采集模块;指纹图像预处理模块;特征提取模块;特征匹配模块。其中,指纹图像预处理模块又包括:图像质量评估,图像分割、图像增强、细化、二值化等步骤。
指纹识别技术中,图像增强技术是其中一个非常重要的步骤。如果指纹图像得不到准确、显著的增强,指纹特征就难以被准确提取。许多学者对指纹图像增强方法进行了探讨,其中,Coetzee等使用Marr-Hildreth边缘算子得到指纹灰度图的脊边缘图,提出了采用卷积模板来进行增强的方法,Randolph等提出了一种使用方向滤波器组来对二值图像来进行增强的方法,Sherlock等提出了采用傅立叶滤波器来增强指纹图像的方法,Hang提出了使用Gabor滤波器的方法来增强指纹图像的方法。事实证明,这些方法用于金融安全、数字加密、电子商务等安保领域都取得了较好的效果。
但是,这些方法均为通过对指纹图像进行局域分析来估计指纹方向图,而后对图像进行滤波增强。当系统中输入的指纹源图像质量较低时,其分辨率会大大降低,这些方法难以准确得到局域的方向图估计值,将无法进行有效的滤波增强。实质上,司法领域中所需识别的指纹图像大多遭到严重污染,分辨率较低,因此,现有的图像增强算法大多难以在该领域发挥大的作用。
目前,现有的指纹识别技术在处理低质量指纹方面的效能明显较低,难以满足鉴定的需求。这主要是因为,现有指纹识别技术的最关键环节——图像增强技术大多是针对较高质量的指纹图像而设计的,对严重污染分辨率低的低质量指纹图像难以达到理想的增强效果。
针对此情况,为增强指纹识别系统处理低质量指纹的能力,在总结相关研究成果的基础上,本文提出了一套新的指纹图像增强算法。该套算法分为三部分:一是纹理信息放大算法。此算法通过改进局部直方图均衡化算法的累积分布函数而得,其中包含一个非线性的指纹纹理信息放大器,用于放大分辨率低的指纹纹理信息,增强纹线对比度,有助于提高鉴定人员对模糊指纹判别的准确性;二是指纹方向图二步算法。此算法通过改进经典方向图算法而得,可基于指纹纹理放大图获取方向图。此算法在计算脊点方向后再计算谷点方向,因此具备一定的抗噪性能。而且,此算法引入直方图法计算谷点方向,对方向图的平滑滤波进一步提高了方向图的准确性;三是方向图修正算法,此算法结合指纹纹线特征,通改进经典的块方向图算法而得,可在更大的邻域窗口中计算块方向图。与传统块方向图算法相比,此算法的处理窗口可比其大3至5倍。当指纹图像出现大面积污染时,由于大邻域窗口中信息量大,采用此算法仍有较大可能得出准确的方向图,据此进行滤波,就能得到更加清晰、准确的指纹图像。这些算法修正方向图的能力有了较大的提高。
大量实验表明,对于指纹图像中块面积大于7×7个象素的低质量区域,现有的经典指纹图像增强算法是难以进行理想的增强和修复的。本文所述纹理放大算法可修复7×7个象素以下大小的低质量块,二步方向算法可修复9×9个象素以下大小的低质量块,方向修正算法则可修复13×13个象素以下大小的低质量块,相对传统算法而言,其图像增强效能明显提高。由此可见,本文提出的方法具有更好的增强效果,在处理低质量指纹图像时,显示出明显的优越性。
本文所述算法虽能够在较大局部窗口内修正方向图,但依然不具备从指纹宏观整体入手修正方向图的能力。因此,对于更大面积的低质量块,本文所述算法仍不能给予理想的修复和增强,需要进一步结合指纹自身的特点,不断扩展算法的处理窗口,使之具备从宏观上修正点方向图的能力。
Fingerprint is a kind of flower patterns formed by mastoid ridge lines that grow on the cutis of finger tail end, which have the characteristics that a piece finger of each one is different and settling invariability for life. Fingerprint has great value for person identification. In the near century, after investigating and exploring unceasingly, people have clear cognition on the characteristic system of fingerprint, classify the fingerprint characteristics and put forward the scientific fingerprint identification thereunder and procedure.
At present, fingerprint identification technology (FIT) takes up important station in modern biology identification technologies. Looking from the practicability and the feasibility, FIT can automatically accomplish a series of works, such as fingerprint classification, character distilling, images memory, search, contrast and matching efficiently, swiftly and conveniently. FIT is of the advantages of convenience, high efficiency, impersonality and security, which is more excellent than identification technologies and has been considered as a kind of perfect identification technology.
From the 1960's, computer technology enters into fingerprint identification sight. The countries which have developed computer technology, such as the UK, USA, France and Japan, develop various FIT successively and opened up new ways for fingerprint identification. At present, FIT has been applied widely in financial security, digital encryption; electronic business and judicial practice, which will play more and more important effect in future.
In recent years, with the rapid development of the digital image-processing and the technology of hardware, FIT has attained largish development. But it also can not meet the need of society development. In the 21st, replacing other identification technologies (such as seal, key, code and signature) by FIT far-rangingly will become a significant topic. The research of FIT has become a focus among the field of model recognition, manipulating an image and computer vision.
Usually, FIT uses general characters of fingerprints to carry through species identification. Basing on it, FIT will systemically compare the detail Characters of fingerprints, and then judge that the fingerprints is whether identical or not. FIT include the modules following: Fingerprint image collection, image pretreatment, character pick-up and character matching. Image pretreatment include the steps following: image quality evaluation, image segmentation, image enhancement, fragmentation, binarization and so on.
In the identification technology, image enhancement technology is a all-important one. If fingerprint image is not enhanced effectively, fingerprint's features can not be extracted. Many scholars have discussed fingerprint image enhancement modes. Coetzee etc use Marr-Hildreth edge operator to gain the ridge margin image of fingerprint grey chat and bring forward a method that adopts convolution template to enhance the image. Randolph etc bring forward a method that adopts a set of directional filters to enhance the binary image. Sherlock etc bring forward a method that adopts Fourier filter to enhance the image. Hang etc bring forward a method that adopts Gabor filter to enhance the image. Proof by facts, used in finance security, digital encrypts, electronic business fields, these methods can gain preferable effect.
However, these methods estimate fingerprint directed graph by analyzing fingerprint image locally, and then enhance the image by filtering. When the source fingerprint image quality is inferior whose resolving power reduces greatly, these methods can't gain local directed graph well and truly and put up effective filtering enhancement. Virtually, fingerprint images which are polluted badly just have low resolution in judicature field. Therefore, the most algorithms at present are difficult to exert effect.
At present, in the aspect of dealing with low quality fingerprint, the efficacy of existing FIT is obviously inferior and difficult to satisfy the need of identification. The primary reason is that the most pivotal tache of existing FIT——image enhancement technologies are devised for high quality fingerprint images, which can not gain perfect effect at enhancing lower quality fingerprint images.
Aiming at this complexion, in order to advance the efficiency of FIT for dealing with low quality images, this article puts forward a suit of new fingerprint image enhancement algorithms. The algorithms have three portions: The first one is texture information amplification algorithm that is gained by ameliorating the cumulative distribution function of local area histogram equalization algorithm and includes a non-linear fingerprint texture information amplifier, can be used to the faint texture information of the low quality fingerprints, enhance texture contrast; The second one is fingerprint directed graph two steps algorithm. It can be gained by ameliorate classics directed graph algorithm and calculate directed graph with texture amplification image. It calculates valley points' direction after calculating ridge points' direction and has the capability of clearing up noise at a certain extent. Moreover, it adopts histogram arithmetic to calculate valley points' direction and carries through smoothing filtering for directed graph, which can improve the veracity of directed graph ulteriorly. The third one is directed graph correction algorithm. It is gained by ameliorate classics piece directed graph algorithms and can calculate piece directed graph in bigger neighborhood. Compared with the traditional directed graph algorithms, its calculation window is 3 to 5 times bigger than them. When the image is polluted in a large scale, it is more possible to work out exact directed graph by this arithmetic than traditional algorithms and gain more clear-cut, exact fingerprint image by filtering. These algorithms' capacity for repairing directed graph has biggish improvement.
A mass of experiments indicate that, it is difficult for existing classical fingerprint image enhancement algorithms to enhance and repair the low quality blocks whose area is bigger than 7×7 pixels, the texture amplification algorithm can repair the blocks whose area is less than 7×7 pixels, the directed graph two steps algorithm can repair the blocks whose area is less than 9×9 pixels, the directed graph correction algorithm can repair the blocks whose area is less than 13×13 pixels perfectly. Compared with traditional algorithms, the image enhancement efficiency of these algorithms is advanced evidently. The algorithms put forward in this article have better effect and reveal obvious advantages when dealing with low quality fingerprint images.
Although the algorithms put forward in this article can repair directed graph in a larger local window, they still can not do that macroscopically. Therefore, these algorithms still can not repair and enhance the low quality blocks whose area is bigger than 13×13 pixels perfectly, need to expand the algorithm's treatment window based on fingerprint's characteristics.
引文
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[8]Alex,Stark J.Adaptive image contrast enhancement using generalizations of histogram equalization[J].IEEE Trans actions on Image Processing,2000,9(5):889-896.
[9]周永东.指纹图像增强算法研究和加速器设计.浙江大学硕士毕业论文.2006
[10]Anil Jain,On-Line Fingerprint Verification[J],IEEE Trans.PAMI.1997.4(19):302-314.
[11]L.Coetzee and E.C.Botha,Fingerprint Recognition in Low Quality Images[J],Pattern Recognition,26(10),1993,1411-1460.
[12]Ranolph T R,Smith M J.Fingerprint image enhancement using a binary angular representation.Acoustics,Speech and Signal Processing[C]. USA:Salt Lake City, 2001,3,1561-1564
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[14] Sherlock D, Monro D M , Millard K. Fingerprint enchancement by directional fourier filtering[J], IEEE proc. Visual Image Signal Processing, 141(2),1994,87-94
[15] Hong L, Wan Y, Jain A K, Fingerprint image enhancement: algorithm and performance evaluation[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(8),1998,777-789
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[25] Alex Stark J. Adaptive image contrast enhancement using generalizations of histogram equalization [J]. IEEE Trans actions on Image Processing,2000,9(5):889-896.
[26]Yin Y L,Ning X B,Zhang X M.Development and application of automatic fingerprint identification technology[J].Natural Sciences,2002,38(1):29-35
[27]Jain A K,Prabhakar S,Hong L.Filter bank based fingerprint matching[J].IEEE Transactions on Image Processing,2000,9(5):846-859
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[29]尹义龙,詹小四,谭台哲等.基于加博函数的指纹增强算法及其应用[J].软件学报,2003,14(3):484-489.
[30]罗希平,田捷.自动指识别中的图像增强和细节匹配算法[J].软件学报,2002,13(5):947-956.
[31]JainA,PrabhakarS,HongL.Filterbank-based fingerprint matching[J].IEEE Transon Image Proc-essing,2000,9(5):847-856.
[32]HONGL,WANGY,JAINA.Finger print image enhancement:algorithm and performance evaluation[J].IEEEPAMI,1998,20(8):777-789.
[33]ICawagoe Masahiro,Tojo Akio.Fingerprint pattern classification[J].Pattern Recognition,1984,17(3):295-303.
[34]AnilJain,LinHong,Pankant.iBiometrics Identification[C].Communications of the ACM(Association for Computing Machinery,2000;43(2):91-98.
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[36]Mehtre B M.Segtmentation of fingerprint image using the directional image[J].Pattern Recognition 1987,20(4):429-435.
[37]HeYuliang,TianJie,LuoXiping,ZhangTanghui.Image enhancement and minutiae matching in finger print verification[J].Pattern Recognition Letters,2003,24(9-10):1349-1360.
[38]PARK S,SMITH M J T and JUN Jae Lee.Fingerprint enhancement based on the directional filter bank[R].Image Proceedings,2000.
[39]Rano 1 ph T R,Smith M J.Fingerprint image enhancement using a binary angular representation,Acoustics,Speech and Signal Processing[C].USA:Salt Lake City,2001,3,1561-1564
[40]Bazen Asker M,Gerez Sabih H.Segmentation of fingerprint images[A].In:[ly1]ProRISC[ly2]2001 Workshop on Circuits,Systems and Signal Processing[C].Veldhoven,TheNetherlands,November,2001.
[41]Ran C,Black K.Type classification of fingerprints:a syntactic approach[J],IEEE Trans.Pattern Analysis and Machine InVlac:hinc Intelligence,1980,2(3):223-231
[42]Bazen A M,Gerez S II.Systematic methods for the computation of the directional fields and singular points of fingerprints[J].IEEE.Transactions on Pattern Analysis and Machine Intelligence,2002.24(7):905-919
[43]Hong Lin,Wan Yifei,Jain A.Fingerprint image enhancement algorithm and performance evaluation[J].IEEE.Transactions on Pattern Analysis and Machine Intelligence,1998.20(8):777-789
[44]Jain A.K,Ross A,Prabhakar S.Fingerprint matching using minutiae and texture features.Proceedings International Conference on Image Processing(ICIP),Greece,2001:282-285
[45]O'Gorman L,Nickersion J V.An approach to fingerprint filter design[J].Pattern Recognition,1989,22(1):29-38
[46]张明,吴陈,陈楠.基于方向滤波的指纹图像增强算法[J],微机发展,2005.6(30):85-87
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[5]AnilJain,LinHong,Pankant.iBiometrics Identification[C].Communications of the ACM(Association for Computing Machinery,2000;43(2):91-98.
[6]N.Bonnet and J.Cutrona,M.Herbin,A'No-Threshold'Histogram-Based Image Segmentation Method.Pattern Recogmition.29(7),1996:1221-1231.
[7]B.M,Mehtre,B.Chiatterjee.Segmentation of Finherprint Images-A Composite Method[J].Pattern Recognition,1989,22(4):381-385.
[8]Alex,Stark J.Adaptive image contrast enhancement using generalizations of histogram equalization[J].IEEE Trans actions on Image Processing,2000,9(5):889-896.
[9]周永东.指纹图像增强算法研究和加速器设计.浙江大学硕士毕业论文.2006
[10]Anil Jain,On-Line Fingerprint Verification[J],IEEE Trans.PAMI.1997.4(19):302-314.
[11]L.Coetzee and E.C.Botha,Fingerprint Recognition in Low Quality Images[J],Pattern Recognition,26(10),1993,1411-1460.
[12]Ranolph T R,Smith M J.Fingerprint image enhancement using a binary angular representation.Acoustics,Speech and Signal Processing[C]. USA:Salt Lake City, 2001,3,1561-1564
[13] L. O'Gorman and J. V. Nickorson, An Approach to Fingerprint Filter Design[J], Pattern Recognition, 22(1),1989,29-38
[14] Sherlock D, Monro D M , Millard K. Fingerprint enchancement by directional fourier filtering[J], IEEE proc. Visual Image Signal Processing, 141(2),1994,87-94
[15] Hong L, Wan Y, Jain A K, Fingerprint image enhancement: algorithm and performance evaluation[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(8),1998,777-789
[16] R.J.Wall, A.Klinger, and K.R.Castleman, Analysis of Image Histograms [C], Copenhagen, 1974
[17] A.P.Dhawan, G.Buelloni, andR.Gordon, Enhancement of mammographic features by optimal adaptive neighborhood image processing[J], IEEE Trans. Medical Imaging S(1), 1986, 8-15.
[18] A. P. Dhawan and E. LeRoyer, Mammographic feature enhancement by computerized image processing[J], Comput. Methods Progr. Biomed. 27(1),1988, 23-35.
[19] E. H. Hall, Almost uniform distribution for computer image enhancement[J],IEEE Trans. Comput. 23(2), 1974,207-208.
[20] D. J. Ketchum, Real-time image enhancement techniques [J], Proc. SPIEIOSA, 1976,120-125.
[21] J.D. Fahnestock and R. A: Schowengerdt, Spatially Variant Contrast Enhancement Using Local Range Modification, Optical Eng. 22(3), 1983, 378-381.
[22] J.A.Stark and W.J.Fitzgerald, Model-based adaptive histogram equalization[J], Signal Process, 1994.1(39): 193-200
[23] J.A.Stark and W.J.Fitzgerald, An alternative algorithm for adaptive histogram equalization[J], Graph. Models Image Process, 1996.3(58):180-185
[24] R. A. Hummel, Image enhancement by histogram transformation [J], Comput. Graphics Image Process. 6,1977,184-195.
[25] Alex Stark J. Adaptive image contrast enhancement using generalizations of histogram equalization [J]. IEEE Trans actions on Image Processing,2000,9(5):889-896.
[26]Yin Y L,Ning X B,Zhang X M.Development and application of automatic fingerprint identification technology[J].Natural Sciences,2002,38(1):29-35
[27]Jain A K,Prabhakar S,Hong L.Filter bank based fingerprint matching[J].IEEE Transactions on Image Processing,2000,9(5):846-859
[28]TabassiE,WilsonCL,Watson C I.Fingerprint image quality[R].Gaithersburg:National Institute of Standard Technology,NISTIR 7151,2004
[29]尹义龙,詹小四,谭台哲等.基于加博函数的指纹增强算法及其应用[J].软件学报,2003,14(3):484-489.
[30]罗希平,田捷.自动指识别中的图像增强和细节匹配算法[J].软件学报,2002,13(5):947-956.
[31]JainA,PrabhakarS,HongL.Filterbank-based fingerprint matching[J].IEEE Transon Image Proc-essing,2000,9(5):847-856.
[32]HONGL,WANGY,JAINA.Finger print image enhancement:algorithm and performance evaluation[J].IEEEPAMI,1998,20(8):777-789.
[33]ICawagoe Masahiro,Tojo Akio.Fingerprint pattern classification[J].Pattern Recognition,1984,17(3):295-303.
[34]AnilJain,LinHong,Pankant.iBiometrics Identification[C].Communications of the ACM(Association for Computing Machinery,2000;43(2):91-98.
[35]Kass M,Witkin A.Analyzing oriented patterns[J].Computer Vision Graph and Image Processing 1987,37(3):362-385.
[36]Mehtre B M.Segtmentation of fingerprint image using the directional image[J].Pattern Recognition 1987,20(4):429-435.
[37]HeYuliang,TianJie,LuoXiping,ZhangTanghui.Image enhancement and minutiae matching in finger print verification[J].Pattern Recognition Letters,2003,24(9-10):1349-1360.
[38]PARK S,SMITH M J T and JUN Jae Lee.Fingerprint enhancement based on the directional filter bank[R].Image Proceedings,2000.
[39]Rano 1 ph T R,Smith M J.Fingerprint image enhancement using a binary angular representation,Acoustics,Speech and Signal Processing[C].USA:Salt Lake City,2001,3,1561-1564
[40]Bazen Asker M,Gerez Sabih H.Segmentation of fingerprint images[A].In:[ly1]ProRISC[ly2]2001 Workshop on Circuits,Systems and Signal Processing[C].Veldhoven,TheNetherlands,November,2001.
[41]Ran C,Black K.Type classification of fingerprints:a syntactic approach[J],IEEE Trans.Pattern Analysis and Machine InVlac:hinc Intelligence,1980,2(3):223-231
[42]Bazen A M,Gerez S II.Systematic methods for the computation of the directional fields and singular points of fingerprints[J].IEEE.Transactions on Pattern Analysis and Machine Intelligence,2002.24(7):905-919
[43]Hong Lin,Wan Yifei,Jain A.Fingerprint image enhancement algorithm and performance evaluation[J].IEEE.Transactions on Pattern Analysis and Machine Intelligence,1998.20(8):777-789
[44]Jain A.K,Ross A,Prabhakar S.Fingerprint matching using minutiae and texture features.Proceedings International Conference on Image Processing(ICIP),Greece,2001:282-285
[45]O'Gorman L,Nickersion J V.An approach to fingerprint filter design[J].Pattern Recognition,1989,22(1):29-38
[46]张明,吴陈,陈楠.基于方向滤波的指纹图像增强算法[J],微机发展,2005.6(30):85-87