基于灰度特性的指纹图像预处理的研究
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摘要
在自动指纹识别系统中,由于各种原因使得采集到的指纹图像有10%左右是低质量指纹图像,这对细节特征的提取造成了困难,同时也影响了特征提取的准确性。为保证指纹识别算法对指纹质量的鲁棒性,需对采集的指纹图像进行预处理处理以提高指纹图像的质量。
论文对指纹图像的预处理进行了研究,其主要工作如下:首先考虑对指纹的分割处理,在进过分析了基于灰度统计的分割算法后,将自适应均值和标准差的比值作为阀值分割的标准,并在分析了残留区域与有效区域的关系后,引入了轮廓跟踪来进行二次分割;其次着重从方向图计算和滤波处理两个方面对指纹图像的增强进行研究,在方向图的计算时,通过对几种基于直接量化法的方向图的计算方法进行分析和比较,给出了改进的算法,然后在方向图的基础上设计了一套方向模板对指纹图像进行滤波;最后对多种细化的算法进行比较、分析,结合快速细化算法和OPTA算法对二值化图像进行细化处理。
实验结果表明,论文中的算法能够有效的将指纹的有效区域分割出来,在噪声的影响下可以更准确的求取图像的方向图,通过增强处理可以有效地增强指纹图像中沿着脊线方向上的信息,减弱与脊线方向垂直方向上的信息。细化后的骨架接近纹线,更有利于特征点的提取。
In practice, due to kinds of cause, a 10 percentage of acquired fingerprint images are of poor quality by fingerprint image collecting in the system of automated finger identification. It's hard to extract the minutia from the poor quality fingerprint, and also the accuracy of minutia is affected. In order to ensure that the performance of the quality of input fingerprint image, a series of processes are needed to enhance the quality of fingerprint image.
The paper discussed the fingerprint image preprocessing. The main research are as following: In the step of the segmentation, after analysis the method of segmentation based on gray-scale statistic, the segmentation use the ratio of the adaptive average value and standard deviation as the threshold of standard and then import the boundary of tracking after discussing the relation of remain region and effective region. And then the fingerprint enhancement is discussed from the side of direction computation and process of filtering, after comparing some methods of directional image computation, an improved method is proposed based on joint cutting method, designing a set of directional template to filter the fingerprint image. The set of methods can enhance the quality of fingerprint image effectively. After comparing and analyzing some methods of binarization and thinning, locally adaptive thresholding is chosen to binarize the enhanced image, and combining the quick thinning algorithm and improved OPTA thinning for the process of thinning.
Experiments show that the methods of preprocessing in this paper have good results, which have effectively divided foreground form the fingerprint image and computed direction of fingerprint under the circumstance of voice. And after enhancement, the information on the ridgeline is effectively enhanced, while the vertical is weakened. The skeleton after thinning is convenient to extracting character form it.
论文对指纹图像的预处理进行了研究,其主要工作如下:首先考虑对指纹的分割处理,在进过分析了基于灰度统计的分割算法后,将自适应均值和标准差的比值作为阀值分割的标准,并在分析了残留区域与有效区域的关系后,引入了轮廓跟踪来进行二次分割;其次着重从方向图计算和滤波处理两个方面对指纹图像的增强进行研究,在方向图的计算时,通过对几种基于直接量化法的方向图的计算方法进行分析和比较,给出了改进的算法,然后在方向图的基础上设计了一套方向模板对指纹图像进行滤波;最后对多种细化的算法进行比较、分析,结合快速细化算法和OPTA算法对二值化图像进行细化处理。
实验结果表明,论文中的算法能够有效的将指纹的有效区域分割出来,在噪声的影响下可以更准确的求取图像的方向图,通过增强处理可以有效地增强指纹图像中沿着脊线方向上的信息,减弱与脊线方向垂直方向上的信息。细化后的骨架接近纹线,更有利于特征点的提取。
In practice, due to kinds of cause, a 10 percentage of acquired fingerprint images are of poor quality by fingerprint image collecting in the system of automated finger identification. It's hard to extract the minutia from the poor quality fingerprint, and also the accuracy of minutia is affected. In order to ensure that the performance of the quality of input fingerprint image, a series of processes are needed to enhance the quality of fingerprint image.
The paper discussed the fingerprint image preprocessing. The main research are as following: In the step of the segmentation, after analysis the method of segmentation based on gray-scale statistic, the segmentation use the ratio of the adaptive average value and standard deviation as the threshold of standard and then import the boundary of tracking after discussing the relation of remain region and effective region. And then the fingerprint enhancement is discussed from the side of direction computation and process of filtering, after comparing some methods of directional image computation, an improved method is proposed based on joint cutting method, designing a set of directional template to filter the fingerprint image. The set of methods can enhance the quality of fingerprint image effectively. After comparing and analyzing some methods of binarization and thinning, locally adaptive thresholding is chosen to binarize the enhanced image, and combining the quick thinning algorithm and improved OPTA thinning for the process of thinning.
Experiments show that the methods of preprocessing in this paper have good results, which have effectively divided foreground form the fingerprint image and computed direction of fingerprint under the circumstance of voice. And after enhancement, the information on the ridgeline is effectively enhanced, while the vertical is weakened. The skeleton after thinning is convenient to extracting character form it.
引文
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[2]高倩倩.指纹识别预处理算法研究:[硕士学位论文].成都:电子科技大学,2007
[3]Clarke R.Human.Identification in Information System:Management Challenges and Public Policy Issue.Information Technology and People.1994,7(4):6-37
[4]夏鸿斌,须文波,刘渊.生物特征识别技术研究进展.计算机工程与应用,2003,39(20):77
[5]杨秀.关于生物识别技术[EB/OL].http://www.stcsm.gov.cn/leaming/lesson/shengwu/20050117/lesson-1.asp,2005-01-17/2007-01-08
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[7]荣海泓,席志红.生物特征身份鉴别技术应用及发展趋势.现代电子技术,2004,27(10):88-92
[8]刘少聪.新指纹学[M].安徽:人民出版社,1984.10-11
[9]指纹奇妙世界[EB/OL].http://88say.udn.corn/default.aspx?uclId=product[pq]397[pq]result_397_1
[10]Anil Jain,Sharath Pankanti.Fingerprint Classification and Matching.In A Bovik,editor,Handbook for Image and Video Processing.Academic Press,2000.59-60
[11]中国生物特征认证动态.北京:创世纪号,2003.1-10
[12]李增栋.基于小波分析的指纹识别系统预处理的算法研究[硕士学位论文].上海:东华大学,2007
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[17]Mital D P,Khwang E.An Automated Matching Technique for Fingerprint Identification.In:First International Conference on Knowledge-based Intelligent Electronic Systems.Australia:KES,1997.21-23
[18]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):913-920
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[20]马义德,段磊,韩明秋等.指纹图像压缩方法的研究.甘肃科学学报,2005,17(2):29-31
[21]王玮,李见为,张腾.指纹图像的预处理算法[J].计算机应用,2004,24(5):74-77
[22]刘兴龙.自动指纹识别系统的研究与实现[硕士学位论文].贵州:贵州大学,2006
[23]郑潇.指纹图像预处理算法的研究[硕士学位论文].辽宁:沈阳工业大学,2006
[24]刘灵丽.指纹图像预处理与特征提取[硕士学位论文].湖南:湖南大学,2005
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[28]B.M.Mehtre,B.Chatterjee.Segmentation of Fingerprint Images-A Composite Method.Pattern Recognition,1989,22(4):381-385
[29]唐良瑞,谢晓辉,蔡安妮等.基于D-S证据理论的指纹图像分割算法.计算机学报,2003,26(7):887-892
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[31]王彦荣,陈旭,于飞等.基于像素分类的指纹图像分割方法.第四界中国生物识别会议,2003,160-164.
[32]林国清,李见为,王崇文.指纹图像预处理方法的研究.光电工程,2002,29(5):56-58
[33]Bemd Fischer,Joachim.Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation.IEEE Trans on Pattern Analysis and Machine Intelligence,2003,25(4):513-518
[34]R.M.Haralick,L.G.Shapiro.Image segmentation techniques.ComputerVision,Graphics,and Image Processing,1985,29(1):100-132
[35]N.R.Pal,S.K.Pal.A review on image segmentation techniques.Pattern Recognition,1993,26(9):1277-1294
[36]章毓晋.图像分割.北京:科学出版社,2001.20-40
[37]何余良,田捷,张小鹏.基于马尔科夫的指纹图像分割方法.见:彭群生,编.第四届中国计算机图形学大会(ChinaGraph 2002)会议论文集.北京:电子工业出版社,2002.149-156
[38]L.O'Gorman,J.Nickerson.An approach to fingerprint filter design.Pattern Recognition,1989,22(1):29-38
[39]B.M.Mehtre,Fingerprint image analysis of automatic identification.Machina Vision,Appl,1993,22(6):124-139
[40]D.Maio and D.Maltoni,Neural network based minutiae filtering in fingerprint.In:Proc.of 14~(th)Internat.Conf.Pattern Recognition,Australia:Brisbane,1998,1654-1658
[41]Andres Almansa,Tony Lindeberg.Fingerprint Enhancement by Shape Adaptation of Scale-Space Operators with Automation Scale Selection.IEEE Trans:On Image Processing,2000,9(12):2027-2042.
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