基于人眼自然睁开状态下的虹膜识别方法研究
|
摘要
随着现代化技术的不断出现,传统方法已经不能满足当今社会对身份识别的要求。这样,就使得生物特征识别技术得以迅速发展起来。生物特征识别具有传统方法所无法比拟的高准确性、高安全性、高可靠性等优点,能够满足社会各个领域的需求。虹膜身份识别是一种新兴的生物身份识别技术,由于其具有唯一性、稳定性、可采集性、非侵犯性等优点而逐步受到人们的重视。与脸像、声音、指纹等身份鉴别方法相比,虹膜具有更高的准确性。近些年来虹膜识别技术在研究和应用方面都得到了长足的进步,并表现出了广阔的前景和市场。
虹膜识别系统主要包括了虹膜图像采集、虹膜图像预处理、虹膜图像特征提取、模式匹配与识别。它的研究主要涉及到了计算机视觉、数字图像处理、小波理论、模式识别等众多学科领域。本文在总结目前虹膜识别技术研究进展的基础上,对虹膜识别中的虹膜定位、噪声处理、归一化、特征提取及编码和分类决策问题展开探讨,提出了自己的一些改进方法并进行实验对比,对进一步研究虹膜识别技术有一定的借鉴作用。
在虹膜定位方面,提出一种基于人眼结构特征的定位方法。首先,通过邻域灰度平均值去除光斑,接着通过投影法和瞳孔中心检测算子来找瞳孔内一点,然后通过边缘检测算子结合投票的方法找内外四个边界点准确定位虹膜内外边界。对2655幅人眼图像进行虹膜定位实验的结果证明,该方法能够有效地解决虹膜图像平移问题,并避免了反射光斑、眼睑、眼睫毛等噪声对定位的影响。与经典方法相比,该方法在保证定位成功率的同时,定位时间大大减少。
在人眼自然睁开状态下存在噪声,因此需要进行去噪操作:通过抛物线拟合的方法去除上、下眼睑;利用邻域灰度均值法去除光斑;去除眼睫毛时先用局部灰度极小值的方法找到所有候选眼睫毛点,然后利用眼睫毛的起始信息、方向信息和长度信息去除伪眼睫毛点。
在归一化方面,将环形虹膜图像通过坐标变换转变为矩形,该方法能够有效地解决虹膜图像缩放问题,同时进行内外圆心不一致的补偿操作,并确定有效虹膜区域,便于进行后续的操作。
在特征提取和编码方面,在有效虹膜区域内,采用基于小波变换系数的特征提取方法,并提出基于局部灰度极小值、基于纹理特征点匹配,基于多方向纹理边缘检测三种结构特征提取方法。基于小波变换系数的特征提取方法采用墨西哥草帽小波和Morlet小波,对不同尺度下的小波变换系数分布进行考察,通过设置不同的尺度,可以得到不同频率下的纹理特征,并分析哪些尺度下得到的虹膜纹理比例要高一些。与传统的小波变换方法相比,本文提出的基于小波变换系数的方法更具有优势,不仅算法的复杂度比较低,而且算法的识别率高;基于局部灰度极小值的特征提取方法利用了虹膜纹理的位置信息和灰度信息,却抛弃了纹理的大小、方向、相关性等其他结构特征,因此提取出来的可区分性特征仍然不能保证使虹膜识别的准确性得到大幅度的提高;基于纹理特征点匹配的特征提取方法虽然利用了纹理的大小与方向信息,但是选取的方向信息少,未考虑纹理之间的相关性,使得识别准确性还有可提高的空间;基于多方向纹理边缘检测的特征提取方法利用了纹理的位置、灰度、大小、方向、相关性等多种结构特征,因此提取出来的可区分性特征使虹膜识别的准确性得到大幅度的提高。
在模式匹配与识别方面,采用自行设定的计算距离的方法来计算登陆虹膜码与注册虹膜码之间的距离,通过多次平移匹配来解决图像旋转的问题,通过阈值法进行分类。
最后,在已有的图库上,对基于Gabor滤波器的方法进行了实验,并将本文提出的方法与基于Gabor滤波器的方法进行了比较和分析。
With the constant development of modern technologies, traditional methods of personal recognition can not satisfy various demands of the present society. Under this condition, the personal recognition based on the biometric begins to develop rapidly. The rising biometrics has a lot of advantages which are not involved in traditional methods, such as high accuracy, high security, high reliability, and so on. Biometrics also satisfies many requirements of each field in our society. Iris recognition is an emerging biometric technology. For its exclusive, stability, collectible and unforced, iris recognition is being more and more regarded by people. In recent years, iris recognition has made progress in technology research and application, and has a wide prospect and market. Compared with face, voice, fingerprint and other biometric technology, iris recognition has higher precision.
An iris recognition system includes iris imaging, iris preprocessing, feature extraction, pattern match and classifying. Its research aspects involve many subjects such as computer vision, digital image processing, wavelet theory, pattern recognition. Based on the recent advancements in iris recognition, this article makes discussion about iris localization, noise processing, normalization, feature extraction, code and classifying decision-making, and presents some improving methods and the experimental results are also described. These use for references for more research on the iris recognition technology.
In iris localization, a method of iris localization based on the human eye structure is presented. Firstly, it removes the facula by the average gray of adjacent area and finds a point in the pupil by the projection and pupil-center detection operator, then finds four boundary points in inner and outer boundary respectively by edge detection operator or combining voting method to localize the inner and outer boundaries. Experimental results on 2655 eye images demonstrate that the proposed method of iris localization can effectively resolve the problems of iris translation and it can not be affected by facula, eyelid and eyelashes. Compared with two classical methods, the proposed method ensures the probability of successful localization and reduces the time consumedly.
There is much noise in the state of eyes opening unforcedly, so the noise processing is necessary, the upper and lower eyelids are removed by parabola fitting, the facular is removed by neighboring gray average, the eyelashes are removed by finding all the candidate eyelashes by local gray minimum firstly, then the false eyelashes are removed by the beginning location, directional and length information of the eyelashes.
In normalization, the annular iris image is normalized to rectangular image. It can resolve the problems of iris zoom and solve the disaccord of inner and outer center, and ensure the effective iris area for the next operations.
In feature extraction and code, many feature extraction methods are presented such as based on wavelet transform coefficients, local gray minimum, texture feature points matching, multidirectional texture edge detection. The method based on the wavelet transform coefficients adopts Marr and Morlet wavelets to extract feature based on the wavelet transform coefficients, reviews the coefficients distribution of different scales. It can get feature of different frequency by setting different scale and analyze which iris texture proportion is higher under which scale. This method has more advantages, lower complication and higher recognition rate comparing with traditional wavelet transform methods. The method based on local gray minimum uses the position and gray information of iris texture, but discards other structure characteristic such as size, direction and relativity of texture, so the partial feature can not ensure to increase much more correctness. Although the method based on texture feature points matching uses the size and direction information, the direction information chosen is less and discards the relativity of texture, so there is much more elevated space. The method based on the multidirectional edge detection uses the position, gray, size, direction and relativity of texture, so the dividing features ensure to increase the precision much more.
In pattern match and recognition, it calculates the distance between enroll iris code and register iris code by distance designed by ourselves, solves the rotary problems by many transferring matches along horizontal directions, and classifies by threshold.
Finally the performance of the presented methods and the method based on the gabor filter is analyzed and compared in the V3 Interval database.
虹膜识别系统主要包括了虹膜图像采集、虹膜图像预处理、虹膜图像特征提取、模式匹配与识别。它的研究主要涉及到了计算机视觉、数字图像处理、小波理论、模式识别等众多学科领域。本文在总结目前虹膜识别技术研究进展的基础上,对虹膜识别中的虹膜定位、噪声处理、归一化、特征提取及编码和分类决策问题展开探讨,提出了自己的一些改进方法并进行实验对比,对进一步研究虹膜识别技术有一定的借鉴作用。
在虹膜定位方面,提出一种基于人眼结构特征的定位方法。首先,通过邻域灰度平均值去除光斑,接着通过投影法和瞳孔中心检测算子来找瞳孔内一点,然后通过边缘检测算子结合投票的方法找内外四个边界点准确定位虹膜内外边界。对2655幅人眼图像进行虹膜定位实验的结果证明,该方法能够有效地解决虹膜图像平移问题,并避免了反射光斑、眼睑、眼睫毛等噪声对定位的影响。与经典方法相比,该方法在保证定位成功率的同时,定位时间大大减少。
在人眼自然睁开状态下存在噪声,因此需要进行去噪操作:通过抛物线拟合的方法去除上、下眼睑;利用邻域灰度均值法去除光斑;去除眼睫毛时先用局部灰度极小值的方法找到所有候选眼睫毛点,然后利用眼睫毛的起始信息、方向信息和长度信息去除伪眼睫毛点。
在归一化方面,将环形虹膜图像通过坐标变换转变为矩形,该方法能够有效地解决虹膜图像缩放问题,同时进行内外圆心不一致的补偿操作,并确定有效虹膜区域,便于进行后续的操作。
在特征提取和编码方面,在有效虹膜区域内,采用基于小波变换系数的特征提取方法,并提出基于局部灰度极小值、基于纹理特征点匹配,基于多方向纹理边缘检测三种结构特征提取方法。基于小波变换系数的特征提取方法采用墨西哥草帽小波和Morlet小波,对不同尺度下的小波变换系数分布进行考察,通过设置不同的尺度,可以得到不同频率下的纹理特征,并分析哪些尺度下得到的虹膜纹理比例要高一些。与传统的小波变换方法相比,本文提出的基于小波变换系数的方法更具有优势,不仅算法的复杂度比较低,而且算法的识别率高;基于局部灰度极小值的特征提取方法利用了虹膜纹理的位置信息和灰度信息,却抛弃了纹理的大小、方向、相关性等其他结构特征,因此提取出来的可区分性特征仍然不能保证使虹膜识别的准确性得到大幅度的提高;基于纹理特征点匹配的特征提取方法虽然利用了纹理的大小与方向信息,但是选取的方向信息少,未考虑纹理之间的相关性,使得识别准确性还有可提高的空间;基于多方向纹理边缘检测的特征提取方法利用了纹理的位置、灰度、大小、方向、相关性等多种结构特征,因此提取出来的可区分性特征使虹膜识别的准确性得到大幅度的提高。
在模式匹配与识别方面,采用自行设定的计算距离的方法来计算登陆虹膜码与注册虹膜码之间的距离,通过多次平移匹配来解决图像旋转的问题,通过阈值法进行分类。
最后,在已有的图库上,对基于Gabor滤波器的方法进行了实验,并将本文提出的方法与基于Gabor滤波器的方法进行了比较和分析。
With the constant development of modern technologies, traditional methods of personal recognition can not satisfy various demands of the present society. Under this condition, the personal recognition based on the biometric begins to develop rapidly. The rising biometrics has a lot of advantages which are not involved in traditional methods, such as high accuracy, high security, high reliability, and so on. Biometrics also satisfies many requirements of each field in our society. Iris recognition is an emerging biometric technology. For its exclusive, stability, collectible and unforced, iris recognition is being more and more regarded by people. In recent years, iris recognition has made progress in technology research and application, and has a wide prospect and market. Compared with face, voice, fingerprint and other biometric technology, iris recognition has higher precision.
An iris recognition system includes iris imaging, iris preprocessing, feature extraction, pattern match and classifying. Its research aspects involve many subjects such as computer vision, digital image processing, wavelet theory, pattern recognition. Based on the recent advancements in iris recognition, this article makes discussion about iris localization, noise processing, normalization, feature extraction, code and classifying decision-making, and presents some improving methods and the experimental results are also described. These use for references for more research on the iris recognition technology.
In iris localization, a method of iris localization based on the human eye structure is presented. Firstly, it removes the facula by the average gray of adjacent area and finds a point in the pupil by the projection and pupil-center detection operator, then finds four boundary points in inner and outer boundary respectively by edge detection operator or combining voting method to localize the inner and outer boundaries. Experimental results on 2655 eye images demonstrate that the proposed method of iris localization can effectively resolve the problems of iris translation and it can not be affected by facula, eyelid and eyelashes. Compared with two classical methods, the proposed method ensures the probability of successful localization and reduces the time consumedly.
There is much noise in the state of eyes opening unforcedly, so the noise processing is necessary, the upper and lower eyelids are removed by parabola fitting, the facular is removed by neighboring gray average, the eyelashes are removed by finding all the candidate eyelashes by local gray minimum firstly, then the false eyelashes are removed by the beginning location, directional and length information of the eyelashes.
In normalization, the annular iris image is normalized to rectangular image. It can resolve the problems of iris zoom and solve the disaccord of inner and outer center, and ensure the effective iris area for the next operations.
In feature extraction and code, many feature extraction methods are presented such as based on wavelet transform coefficients, local gray minimum, texture feature points matching, multidirectional texture edge detection. The method based on the wavelet transform coefficients adopts Marr and Morlet wavelets to extract feature based on the wavelet transform coefficients, reviews the coefficients distribution of different scales. It can get feature of different frequency by setting different scale and analyze which iris texture proportion is higher under which scale. This method has more advantages, lower complication and higher recognition rate comparing with traditional wavelet transform methods. The method based on local gray minimum uses the position and gray information of iris texture, but discards other structure characteristic such as size, direction and relativity of texture, so the partial feature can not ensure to increase much more correctness. Although the method based on texture feature points matching uses the size and direction information, the direction information chosen is less and discards the relativity of texture, so there is much more elevated space. The method based on the multidirectional edge detection uses the position, gray, size, direction and relativity of texture, so the dividing features ensure to increase the precision much more.
In pattern match and recognition, it calculates the distance between enroll iris code and register iris code by distance designed by ourselves, solves the rotary problems by many transferring matches along horizontal directions, and classifies by threshold.
Finally the performance of the presented methods and the method based on the gabor filter is analyzed and compared in the V3 Interval database.
引文
[1]Jain A K,Bolle R,Pankanti S,et al.Biometrics:personal identification in networked society.Norwell,MA:Kluwer,1999.(103-121)
[2]Bolle R M,Connell J H,Pankanti S,et al.Guide to biometrics.New York:Springer-Verlag,2003.(3-59)
[3]Jain A K,Ross A,Prabhakar S.An introduction to biometric recognition.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[4]Leonard Flom,Aran Safir.Iris recognition system.US:Patent 4641349,1987.
[5]Johnson R G,Can iris patterns be used to identify people,Los Alamos National Laboratory,CA,Chemical and Laser Sciences Division,Rep.LA-12331-PR,1991.
[6]Daugman J G.High confidence visual recognition of persons by a test of statistical independence.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(1):1148-1161.
[7]Wildes R P,Asmuth J C,Green G L,et al.A system for automated iris recognition.In Proceedings of the Second IEEE Workshop on Applications of Computer Vision.Sarasota,Horida,1994,121-128.
[8]Boles W W,Boashash B.A human identification technique using images of the iris and wavelet transform.IEEE Transactions on Signal Processing,1998,46(4):1185-1188.
[9]Kee G,Byun Y.Improved techniques for an iris recognition system with high performance.Lecture Notes Artificial Intelligence,2001,11:177-188.
[10]Ma L,Wang Y,Tan T.Iris Recognition Based on Multichannel Gabor Filtering.The 5th Asian Conference on Computer Vision.2002,1:1-5
[11]Daugman J G.Statistical richness of visual phase information:Update on recognizing persons by iris patterns.International Journal of Computer Vision,2001,45(1):25-38.
[12]Daugman J G.Demodulation by complex-valued wavelets for stochastic pattern recognition.International Journal of Wavelets,Multiresolution and Information Processing,2003,1(1):1-17.
[13]苗守仁,刘希顺,刘安芝.一种快速精确的虹膜定位方法.微计算机信息.2005,21(1):154-156
[14]沈沉,林斌,汪林峰.虹膜识别技术中的图像处理.光学仪器.2004,26(1):44-48
[15]李庆嵘,马争.虹膜定位算法研究.电子科技大学学报.2002,31(1):7-9
[16]何家峰,廖曙铮,叶虎年,等.虹膜定位.中国图像图形学报.2005,5(A):253-255
[17]陈志坚,朱峰,李峰.基于统计的虹膜定位算法改进.计算技术与自动化.2005,24(3):45-47
[18]裴晓敏,吴建华.一种快速的虹膜定位算法.仪器仪表学报.2005,8:407-409
[19]余节慰,沈永增,嵇新浩.基于小波的虹膜定位算法的研究.浙江工业大学学报.2005,33(2):181-183
[20]杨文,于力,王宽全,等.虹膜定位的快速算法.计算机工程与应用.2004,10:82-84
[21]范科峰,曾庆宁.虹膜定位算法的研究.计算机工程与应用.2004,2:60-61,74
[22]王琪,费耀平.基于边缘追踪的虹膜定位算法.信息技术.2005,9:41-43
[23]韩方,陈颖,陆亭立.虹膜定位算法.上海大学学报(自然科学版).2001,7(6):501-503
[24]黄小欢,刘辉.基于灰度梯度的虹膜定位方法研究.昆明理工大学学报,2001,26(6):32-34
[25]何家峰,叶虎年,叶妙元.一种改进的虹膜定位方法.计算机工程,2002,28(12):129-130
[26]田启川,潘泉,程咏梅,等.不同光照下的虹膜边界定位研究.光电子·激光,2006,17(4):488-492
[27]Wildes R P.Iris recognition:An emerging biometric technology.In Proceedings of the IEEE,1997,85(9):1348-1363.
[28]Ma L,Tan T,Zhang D,et al.Local intensity variation analysis for iris recognition.Pattern Recognition,2004,37(6):1287-1298.
[29]于秀丽.虹膜定位算法的研究.天津科技大学学报,2004,19(3):49-51,64
[30]王成儒,胡正平,练秋生.一种虹膜定位算法.计算机辅助设计与图形学学报.2002,14(10):950-952
[31]胡正平,王成儒.改进随机Hough变换在虹膜定位中的应用.计算机工程与设计.2003,24(10):37-39
[32]薛白,刘文耀,王金涛,等.虹膜图像预处理算法研究.光电子·激光.2003,14(7):741-744
[33]胡正平,张晔,王成儒.非线性拟合结合交叉参考的迭代虹膜定位方法.计算机辅助设计与图形学学报.2004,16(3):297-300
[34]陈功,周又玲.基于Hough变换的虹膜定位算法.华东理工大学学报.2004,30(2):230-233
[35]Cui J,Ma L,Wang Y,Tan T,et al.A fast and robust iris localization method based on texture segmentation.In Proceedings of SPIE,2004,5404:401-408.
[36]须文波,周黎.一种新的虹膜定位算法.计算机工程,2006,32(10):199-200,233
[37]胡正平.基于多尺度-局部方向轮廓综合的鲁棒虹膜定位算法.电子学报,2007,35(1):131-134
[38]Sun C,Zhou C,Liang Y,et al.Study and improvement of iris location algorithm.In Proceedings of the 2006 International Conference on Biometrics,Hong Kong,China,January 5-7,2006,LNCS 3832:436-442.
[39]殷珊珊,庄镇泉,叶学义,等.基于行列扫描的虹膜定位算法.计算机应用.2004,24(9):10-12
[40]李动恒,殷珊珊,庄镇泉,等.一种利用分块统计的虹膜定位算法.中国图像图形学报.2004, 9(1):35-39
[41]叶学义,庄镇泉,姚鹏,等.基于2维投影的虹膜定位.中国图像图形学报.2005,10(3):305-309
[42]石勇涛,叶虎年,何家峰,等.一种新型的虹膜定位算法.贵州工业大学学报(自然科学版).2003,32(6):42-44
[43]苑玮琦,马军防,狄文彬,等.基于主动轮廓线的虹膜内边界的定位方法.红外与激光工程.2003,32(6):605-609
[44]苑玮琦,马军防,狄文彬.基于主动轮廓线的虹膜定位方法.计算机工程与应用.2003,34:104-107
[45]张鹏飞,李德胜,王祁.一种快速虹膜定位算法的研究.光电子·激光.2005,16(8):997-999,1006
[46]郭永彩,徐梅宣,高潮.一种快速虹膜定位算法.光电工程.2005,32(7):33-37
[47]叶学义,庄镇泉,张云超.一种快速、新颖的虹膜定位算法.计算机工程与应用,2003,(30):54-56
[48]张仁彦,赵国良,马洪涛.虹膜内边缘定位的齿形变换方法.海军工程大学学报,2006,18(1):19-23
[49]马义德,刘映杰,王升哲,等.一种新的虹膜定位方法.计算机工程与应用,2006,(06):80-81,150
[50]Yuan X,Shi P.An iris segmentation procedure for iris recognition.In Proceedings of the 2004Chinese Conference on Biometric Recognition,Guangzhou,China,December 13-14,2004,LNCS 3338:546-553.
[51]田启川,潘泉,程咏梅,等.虹膜识别中噪声的检测和处理方法.计算机工程,2006,32(2):172-174
[52]来毅,路陈红,卢朝阳.用于虹膜识别的眼睑及眼睫毛遮挡检测.计算机辅助设计与图形学学报,2007,19(3):346-350
[53]Kong W,Zhang D.Accurate iris segmentation based on novel reflection and eyelash detection model.In Proceedings of the 2001 International Symposium on Intelligent Multimedia,Video and Speech Processing,Hong Kong,China,May 2-4,2001,263-266.
[54]Huang J,Wang Y,Tan T,et al.A New Iris Segmentation Method for Recognition.Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04),2004(3):554-557
[55]徐梅宣.虹膜图像处理技术研究.重庆大学硕士学位论文.2004
[56]Miyazawa K,Ito K,Aoki T,et al.An iris recognition system using phase-based image matching.In Proceedings of the 2006 IEEE International Conference on Image Processing,Atlanta,USA,October 8-11,2006,325-328.
[57]康浩,徐国治.虹膜纹理的相位编码.上海交通大学学报.1999,33(5)542-544
[58]Ma L,Tan T,Wang Y.Efficient iris recognition by characterizing key local variations.IEEE Transactions on Image Processing,2004,13(6):739-750.
[59]Tisse C,Martin L.Personal identification technique using human iris recognition.Journal of System Research,2003,4:67-75.
[60]Masek L.Recognition of human iris patterns for biometric identification:(Bachelor's thesis).Perth:University of Western Australia,2003.
[61]Ganeshan B,Theckedath D,Young R,et al.Biometric iris recognition system using a fast and robust iris localization and alignment procedure.Optics and Lasers in Engineering,2006,44(1):1-24.
[62]Boles W W.A security system based on human iris identification using wavelet transform.In Proceedings of the First International Conference on Knowledge-Based Intelligent Electronic Systems,Adelaide,Australia,May 21-23,1997,533-541.
[63]Sanchez-Avila C,Sanchez-Reillo R.Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation.Pattern Recognition,2005,38(2):231-240.
[64]Sanchez-Avila C,Sanchez-Reillo R.Iris-based biometric recognition using dyadic wavelet transform.IEEE Aerospace and Electronic Systems Society Magazine,2002,10:3-6.
[65]应忍冬,徐国治.基于小波变换过零检测的虹膜识别技术.上海交通大学学报.2002,36(3):355-358
[66]黄惠芳,胡广书.一种新的基于小波变换的虹膜识别算法.清华大学学报.2003,43(9):1210-1213
[67]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报.2002,28(1):1-10
[68]Zhu Y,Tan T,Wang Y.Biometric personal identification based on iris patterns.In Proceedings of the 15th International Conference on Pattern Recognition,Barcelona,Spain,September 3-7,2000,Ⅱ:801-804.
[69]Noruzi M,Vafadoost M,Moin M.Iris recognition:localization,segmentation and feature extraction based on Gabor transform.In proceedings of International Conference on Computational Science and its Applications,Glasgow,UK,May 8-11,2006,LNCS 3982:1180-1189.
[70]Ma L,Wang Y,Tan T.Iris recognition using circular symmetric filters.In Proceedings of the 6th International Conference on Pattern Recognition,Quebec,Canada,August 11-15,2002,Ⅱ:414-417.
[71]Ma L,Tan T.Personal Identification Based on Iris Texture Analysis.1EEE Transaction on Pattern Analysis and Machine Intelligence.2003,25(12):1519-1533.
[72]Huang J,Ma L,Wang Y,et al.Learned-based Enhancement Model of Iris.In Proceedings of British Machine Vision Conference.2003:153-162.
[73]Huang J,Ma L,Wang Y,et al.Iris Model Based on Local Orientation Description.In Proceedings of Asian Conference on Computer Vision,2004:954-959
[74]徐涛,刘畅,明星,等.基于实数形式Gabor变换的虹膜识别方法.吉林大学学报.2004,34(1):46-49
[75]Lim S,Lee K,Byeon O,et al.Efficient iris recognition through improvement of feature vector and classifier.Electronics and Telecommunications Research Institute Journal,2001,23(2):61-70.
[76]Ali J,Hassanien E.An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory.Advanced Modeling and Optimization.2003,5(2):93-99
[77]Muhaminad Khurrarn Khan,Jiashu Zhang,Shi-Jinn Horng.An Effective Iris Recognition System for Identification of Humans.Multitopic Conference,Proceedings of INMIC 2004,114-117
[78]应忍冬,徐国治.虹膜纹理编码身份识别技术.计算机应用与软件.2001,6:1-10
[79]Zhang P,Zhang S.Fusion of Global and Local Feature Based Iris Recognition.Intelligent Mechatronics and Automation,2004,922-926
[80]Yang W,Yu L,Lu G,etl.Iris Recognition Based on Location of Key Points.The First International Conference on Biometric Authentication,2004,(3072):484-490
[81]孟浩,徐翠平.虹膜识别算法的研究.哈尔滨工程大学学报,2006,27(3):400-403,452
[82]王琪,费耀平.基于小波包分析的虹膜特征提取方法.计算机工程与应用,2006,(13):60-62
[83]才德,严瑛白,金国藩.双多通道虹膜识别系统的研究.光学技术,2006,32(2):308-310
[84]甘俊英,梁宇.小波包分解在虹膜识别中的应用.计算机应用,2006,26(5):1006-1008
[85]侯文君,李杨,都思丹.基于Radon变换的多尺度虹膜识别算法.中国科学技术大学学报,2007,37(2):208-211
[86]魏连鑫,朱伟民,吴登峰,徐涛.一种新的基于多小波变换的虹膜识别算法.吉林大学学报(理学版),2007,45(1):68-72
[87]Alim A,Sharkas M.Tcxture classification of the human iris using artificial neural networks.In Proceedings of the 11th Mediterranean Electrotechnical Conference,Cairo,Egypt,May 7-9,2002,5:580-583.
[88]严民军,齐翔林,汪九云.虹膜识别中的一种神经算法的研究.生物物理学报.2000,16(4):711-716
[89]Wang Y,Han J.his Recognition Using Support Vector Machines.Proceedings of International Symposium on Neural Networks,2004:622-628.
[90]古红英,潘红,吴飞,等.虹膜自相似性识别方法研究.计算机辅助设计与图形学学报,2004,16(7):973-977
[91]孟爱国,章登勇,陈志坚,等.基于小波包变换和支持向量机的虹膜识别方法.计算机工程与设计,2006,27(10):1769-1771,1774
[92]林金龙,石青云.一种基于结构特征的虹膜识别方法.计算机工程与应用.2003.13:83-84,137
[93]张鹏飞,李德胜,王祁.一种新颖的虹膜识别方法.光电工程,2005,32(5):89-92
[94]Mira J,Mayer J.Image Feature Extraction for Application of Biometric Identification of Iris:A Morphological Approach.Proceeding of the ⅩⅥ Brazilian Symposium on Computer Graphics And processing,2003:391-398
[95]陈颖,秦荫桐,韩方等.一种用于虹膜识别的相位匹配算法.上海大学学报.2002,8(1):31-34
[96]王春,叶虎年.虹膜识别算法研究.贵州工业大学学报.2000,29(3):48-52
[97]黄雅平,罗四维,陈恩义.基于独立分量分析的虹膜识别方法.计算机研究与发展.20003,40(10):1451-1454
[98]Huang Y.An Efficient Iris Recognition System.Proceedings of the First International Conference on Machine Learning and Cybernetics.2002,11:450-454
[99]Wang Y,Han J.Iris Recognition Using Independent Component Analysis.Machine Learning and Cybernetics,2005,7:4487-4492
[100]Noh S,Bae K,Park Y,et al.Multiresolution Independence Component Analysis Iris Identification.http://www.kmutt.ac.th/itc2002/CD/pdf/190745/FA1OC/4.pdf
[101]Wang C,Zhang D,Li Y.Iris verification based on fractional fourier transform.Proceedings of 2002 International Conference on Machine Learning and Cybernetics.2002,3:1470-1473
[102]Shang Y.A Novel Personal Biometric Authentication Technique Using Human Iris Based on Fractal Dimension Feature.ICASSP,2003:201-204
[103]殷珊珊.基于分形理论的虹膜识别算法研究.中国科技大学硕士学位论文.2004
[104]Noh S,Bae K,Park Y,et al.A Novel Method to Extract Feature for his Recognition System.AVBPA.2003:862-868
[105]Zhang P,Li D,Wang Q.A Novel his Recognition Method Based on Feature Fusion.2004 IEEE International Conference on Machine Learning and Cybernetics,2004,9:3661-3665
[106]何家峰,叶虎年,叶妙元.一种基于子块图像互相关的虹膜识别方法.计算机工程与应用.2003,7:63-64,131
[107]Lu C,Lu Z.Zemike moment invariant based iris recognition.In Proceedings of the 2004 Chinese Conference on Biometric Recognition,Guangzhou,China,December 13-14,2004,LNCS 3338:554-561.
[108]Yuan X,Shi P.his Feature Extraction Using 2D Phase Congruency.Information Technology and Applications,2005,ICITA 2005,2:437-441
[109]肖志涛,于明,李锵,等.Log Gabor小波性能分析及其在相位一致性中应用.天津大学学报,2003,36(4):443-446
[110]肖鹏峰,冯学智,赵书河,等.一种基于相位一致的高分辨率遥感图像特征检测方法.遥感学报,2007,11(3):303-310
[111]冯象初,甘小冰,宋国乡编.数值泛函与小波理论.西安:西安电子科技大学出版社,2003
[112]梁学章,何甲兴,王新民,等.小波分析.北京:国防工业出版社,2005
[113]成礼智,郭汉伟著.小波与离散变换理论及工程实践.北京:清华大学出版社,2005
[114]Mallat S G,Zhong S.Characterization of Signals from Multiscale Edge.IEEE T-PAM I,1992,14(7):710-732
[115]Mallat S G,Wang H.Singularity Detection and Processing with Wavelets IEEE Transaction on Information Theory.1992.38(2):617-643
[116]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2003
[117]Q.Chen.Image registration and its application in medical imaging.PhD Thesis,Vrije University Brussel,Faculty of Applied Seiences,Brussel,1998
[118]S.Marcelja.Mathematical description of the responses of simple cortical cells.J.Opt.Soc.Am,1980,70/11:1297-1300
[119]J.G.Daugman.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters.Journal of the Optical Society of America A,July 1985,2(7):1160-1169
[120]J.G.Daugman.Six Formal Properties of Two-Dimensional Anisotropic Visual Filters:Structural Principle and Frequency/Orientation Selectivity.IEEE Trans.on Man,and Cyberneticx,Cet.1989,Vol.SMC-13,No.5:882-887
[121]D.Hubel,T.Wiesel.Receptive fields,binocular interaction and functional architecture in the cat's visual cortex.J.Physiol.(London),1962,Vol:106-154
[122]J.A.Movshon,I.D.Thompson,D.J.Tolhurst.Spatial summation in the receptive fields of simple cells in the cat's striate cortex.J.Physiol.(London),1978,283:53-77
[123]D.Van Essen.Hierarchical organization and functional streams in the visual cortex.Annu.Rev.Neurosci.1979,Vol.2:227-263
[124]P.Kruizinga,N.Petkov,S.E.Grigorescu.Comparison of texture features based on Gabor filters.Proceedings of the 10th International Conference on Image Analysis and Processing,Venice,Italy,September 27-29,1999,142-147
[125]Simona E.Grigorescu,Nicolai Petkov,Peter Kruizinga.Comparison of Texture Features
[126]Based on Gabor Filters.IEEE TRANSACTIONS ON IMAGE PROCESSING,OCTOBER 2002,11(10):1160-1167
[127]Cosmin Grigorescu,Nicolai Petkov,Michel A.Westenberg.Contour Detection Based on Nonclassical Receptive Field Inhibition.IEEE TRANSACTIONS ON IMAGE PROCESSING,JULY 2003,12(7):729-739
[128]N.Petkov,P.Kruizinga.Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli:bar and grating cells.1997,83-96
[129]Peter Kruizinga,Nikolay Petkov.Nonlinear Operator for Oriented Texture.IEEE TRANSACTIONS ON IMAGE PROCESSING,OCTOBER 1999,8(10):1395-1407
[130]明星.虹膜识别技术中小波变换的应用原理与方法.吉林大学博士学位论文.2006
[2]Bolle R M,Connell J H,Pankanti S,et al.Guide to biometrics.New York:Springer-Verlag,2003.(3-59)
[3]Jain A K,Ross A,Prabhakar S.An introduction to biometric recognition.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[4]Leonard Flom,Aran Safir.Iris recognition system.US:Patent 4641349,1987.
[5]Johnson R G,Can iris patterns be used to identify people,Los Alamos National Laboratory,CA,Chemical and Laser Sciences Division,Rep.LA-12331-PR,1991.
[6]Daugman J G.High confidence visual recognition of persons by a test of statistical independence.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(1):1148-1161.
[7]Wildes R P,Asmuth J C,Green G L,et al.A system for automated iris recognition.In Proceedings of the Second IEEE Workshop on Applications of Computer Vision.Sarasota,Horida,1994,121-128.
[8]Boles W W,Boashash B.A human identification technique using images of the iris and wavelet transform.IEEE Transactions on Signal Processing,1998,46(4):1185-1188.
[9]Kee G,Byun Y.Improved techniques for an iris recognition system with high performance.Lecture Notes Artificial Intelligence,2001,11:177-188.
[10]Ma L,Wang Y,Tan T.Iris Recognition Based on Multichannel Gabor Filtering.The 5th Asian Conference on Computer Vision.2002,1:1-5
[11]Daugman J G.Statistical richness of visual phase information:Update on recognizing persons by iris patterns.International Journal of Computer Vision,2001,45(1):25-38.
[12]Daugman J G.Demodulation by complex-valued wavelets for stochastic pattern recognition.International Journal of Wavelets,Multiresolution and Information Processing,2003,1(1):1-17.
[13]苗守仁,刘希顺,刘安芝.一种快速精确的虹膜定位方法.微计算机信息.2005,21(1):154-156
[14]沈沉,林斌,汪林峰.虹膜识别技术中的图像处理.光学仪器.2004,26(1):44-48
[15]李庆嵘,马争.虹膜定位算法研究.电子科技大学学报.2002,31(1):7-9
[16]何家峰,廖曙铮,叶虎年,等.虹膜定位.中国图像图形学报.2005,5(A):253-255
[17]陈志坚,朱峰,李峰.基于统计的虹膜定位算法改进.计算技术与自动化.2005,24(3):45-47
[18]裴晓敏,吴建华.一种快速的虹膜定位算法.仪器仪表学报.2005,8:407-409
[19]余节慰,沈永增,嵇新浩.基于小波的虹膜定位算法的研究.浙江工业大学学报.2005,33(2):181-183
[20]杨文,于力,王宽全,等.虹膜定位的快速算法.计算机工程与应用.2004,10:82-84
[21]范科峰,曾庆宁.虹膜定位算法的研究.计算机工程与应用.2004,2:60-61,74
[22]王琪,费耀平.基于边缘追踪的虹膜定位算法.信息技术.2005,9:41-43
[23]韩方,陈颖,陆亭立.虹膜定位算法.上海大学学报(自然科学版).2001,7(6):501-503
[24]黄小欢,刘辉.基于灰度梯度的虹膜定位方法研究.昆明理工大学学报,2001,26(6):32-34
[25]何家峰,叶虎年,叶妙元.一种改进的虹膜定位方法.计算机工程,2002,28(12):129-130
[26]田启川,潘泉,程咏梅,等.不同光照下的虹膜边界定位研究.光电子·激光,2006,17(4):488-492
[27]Wildes R P.Iris recognition:An emerging biometric technology.In Proceedings of the IEEE,1997,85(9):1348-1363.
[28]Ma L,Tan T,Zhang D,et al.Local intensity variation analysis for iris recognition.Pattern Recognition,2004,37(6):1287-1298.
[29]于秀丽.虹膜定位算法的研究.天津科技大学学报,2004,19(3):49-51,64
[30]王成儒,胡正平,练秋生.一种虹膜定位算法.计算机辅助设计与图形学学报.2002,14(10):950-952
[31]胡正平,王成儒.改进随机Hough变换在虹膜定位中的应用.计算机工程与设计.2003,24(10):37-39
[32]薛白,刘文耀,王金涛,等.虹膜图像预处理算法研究.光电子·激光.2003,14(7):741-744
[33]胡正平,张晔,王成儒.非线性拟合结合交叉参考的迭代虹膜定位方法.计算机辅助设计与图形学学报.2004,16(3):297-300
[34]陈功,周又玲.基于Hough变换的虹膜定位算法.华东理工大学学报.2004,30(2):230-233
[35]Cui J,Ma L,Wang Y,Tan T,et al.A fast and robust iris localization method based on texture segmentation.In Proceedings of SPIE,2004,5404:401-408.
[36]须文波,周黎.一种新的虹膜定位算法.计算机工程,2006,32(10):199-200,233
[37]胡正平.基于多尺度-局部方向轮廓综合的鲁棒虹膜定位算法.电子学报,2007,35(1):131-134
[38]Sun C,Zhou C,Liang Y,et al.Study and improvement of iris location algorithm.In Proceedings of the 2006 International Conference on Biometrics,Hong Kong,China,January 5-7,2006,LNCS 3832:436-442.
[39]殷珊珊,庄镇泉,叶学义,等.基于行列扫描的虹膜定位算法.计算机应用.2004,24(9):10-12
[40]李动恒,殷珊珊,庄镇泉,等.一种利用分块统计的虹膜定位算法.中国图像图形学报.2004, 9(1):35-39
[41]叶学义,庄镇泉,姚鹏,等.基于2维投影的虹膜定位.中国图像图形学报.2005,10(3):305-309
[42]石勇涛,叶虎年,何家峰,等.一种新型的虹膜定位算法.贵州工业大学学报(自然科学版).2003,32(6):42-44
[43]苑玮琦,马军防,狄文彬,等.基于主动轮廓线的虹膜内边界的定位方法.红外与激光工程.2003,32(6):605-609
[44]苑玮琦,马军防,狄文彬.基于主动轮廓线的虹膜定位方法.计算机工程与应用.2003,34:104-107
[45]张鹏飞,李德胜,王祁.一种快速虹膜定位算法的研究.光电子·激光.2005,16(8):997-999,1006
[46]郭永彩,徐梅宣,高潮.一种快速虹膜定位算法.光电工程.2005,32(7):33-37
[47]叶学义,庄镇泉,张云超.一种快速、新颖的虹膜定位算法.计算机工程与应用,2003,(30):54-56
[48]张仁彦,赵国良,马洪涛.虹膜内边缘定位的齿形变换方法.海军工程大学学报,2006,18(1):19-23
[49]马义德,刘映杰,王升哲,等.一种新的虹膜定位方法.计算机工程与应用,2006,(06):80-81,150
[50]Yuan X,Shi P.An iris segmentation procedure for iris recognition.In Proceedings of the 2004Chinese Conference on Biometric Recognition,Guangzhou,China,December 13-14,2004,LNCS 3338:546-553.
[51]田启川,潘泉,程咏梅,等.虹膜识别中噪声的检测和处理方法.计算机工程,2006,32(2):172-174
[52]来毅,路陈红,卢朝阳.用于虹膜识别的眼睑及眼睫毛遮挡检测.计算机辅助设计与图形学学报,2007,19(3):346-350
[53]Kong W,Zhang D.Accurate iris segmentation based on novel reflection and eyelash detection model.In Proceedings of the 2001 International Symposium on Intelligent Multimedia,Video and Speech Processing,Hong Kong,China,May 2-4,2001,263-266.
[54]Huang J,Wang Y,Tan T,et al.A New Iris Segmentation Method for Recognition.Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04),2004(3):554-557
[55]徐梅宣.虹膜图像处理技术研究.重庆大学硕士学位论文.2004
[56]Miyazawa K,Ito K,Aoki T,et al.An iris recognition system using phase-based image matching.In Proceedings of the 2006 IEEE International Conference on Image Processing,Atlanta,USA,October 8-11,2006,325-328.
[57]康浩,徐国治.虹膜纹理的相位编码.上海交通大学学报.1999,33(5)542-544
[58]Ma L,Tan T,Wang Y.Efficient iris recognition by characterizing key local variations.IEEE Transactions on Image Processing,2004,13(6):739-750.
[59]Tisse C,Martin L.Personal identification technique using human iris recognition.Journal of System Research,2003,4:67-75.
[60]Masek L.Recognition of human iris patterns for biometric identification:(Bachelor's thesis).Perth:University of Western Australia,2003.
[61]Ganeshan B,Theckedath D,Young R,et al.Biometric iris recognition system using a fast and robust iris localization and alignment procedure.Optics and Lasers in Engineering,2006,44(1):1-24.
[62]Boles W W.A security system based on human iris identification using wavelet transform.In Proceedings of the First International Conference on Knowledge-Based Intelligent Electronic Systems,Adelaide,Australia,May 21-23,1997,533-541.
[63]Sanchez-Avila C,Sanchez-Reillo R.Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation.Pattern Recognition,2005,38(2):231-240.
[64]Sanchez-Avila C,Sanchez-Reillo R.Iris-based biometric recognition using dyadic wavelet transform.IEEE Aerospace and Electronic Systems Society Magazine,2002,10:3-6.
[65]应忍冬,徐国治.基于小波变换过零检测的虹膜识别技术.上海交通大学学报.2002,36(3):355-358
[66]黄惠芳,胡广书.一种新的基于小波变换的虹膜识别算法.清华大学学报.2003,43(9):1210-1213
[67]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报.2002,28(1):1-10
[68]Zhu Y,Tan T,Wang Y.Biometric personal identification based on iris patterns.In Proceedings of the 15th International Conference on Pattern Recognition,Barcelona,Spain,September 3-7,2000,Ⅱ:801-804.
[69]Noruzi M,Vafadoost M,Moin M.Iris recognition:localization,segmentation and feature extraction based on Gabor transform.In proceedings of International Conference on Computational Science and its Applications,Glasgow,UK,May 8-11,2006,LNCS 3982:1180-1189.
[70]Ma L,Wang Y,Tan T.Iris recognition using circular symmetric filters.In Proceedings of the 6th International Conference on Pattern Recognition,Quebec,Canada,August 11-15,2002,Ⅱ:414-417.
[71]Ma L,Tan T.Personal Identification Based on Iris Texture Analysis.1EEE Transaction on Pattern Analysis and Machine Intelligence.2003,25(12):1519-1533.
[72]Huang J,Ma L,Wang Y,et al.Learned-based Enhancement Model of Iris.In Proceedings of British Machine Vision Conference.2003:153-162.
[73]Huang J,Ma L,Wang Y,et al.Iris Model Based on Local Orientation Description.In Proceedings of Asian Conference on Computer Vision,2004:954-959
[74]徐涛,刘畅,明星,等.基于实数形式Gabor变换的虹膜识别方法.吉林大学学报.2004,34(1):46-49
[75]Lim S,Lee K,Byeon O,et al.Efficient iris recognition through improvement of feature vector and classifier.Electronics and Telecommunications Research Institute Journal,2001,23(2):61-70.
[76]Ali J,Hassanien E.An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory.Advanced Modeling and Optimization.2003,5(2):93-99
[77]Muhaminad Khurrarn Khan,Jiashu Zhang,Shi-Jinn Horng.An Effective Iris Recognition System for Identification of Humans.Multitopic Conference,Proceedings of INMIC 2004,114-117
[78]应忍冬,徐国治.虹膜纹理编码身份识别技术.计算机应用与软件.2001,6:1-10
[79]Zhang P,Zhang S.Fusion of Global and Local Feature Based Iris Recognition.Intelligent Mechatronics and Automation,2004,922-926
[80]Yang W,Yu L,Lu G,etl.Iris Recognition Based on Location of Key Points.The First International Conference on Biometric Authentication,2004,(3072):484-490
[81]孟浩,徐翠平.虹膜识别算法的研究.哈尔滨工程大学学报,2006,27(3):400-403,452
[82]王琪,费耀平.基于小波包分析的虹膜特征提取方法.计算机工程与应用,2006,(13):60-62
[83]才德,严瑛白,金国藩.双多通道虹膜识别系统的研究.光学技术,2006,32(2):308-310
[84]甘俊英,梁宇.小波包分解在虹膜识别中的应用.计算机应用,2006,26(5):1006-1008
[85]侯文君,李杨,都思丹.基于Radon变换的多尺度虹膜识别算法.中国科学技术大学学报,2007,37(2):208-211
[86]魏连鑫,朱伟民,吴登峰,徐涛.一种新的基于多小波变换的虹膜识别算法.吉林大学学报(理学版),2007,45(1):68-72
[87]Alim A,Sharkas M.Tcxture classification of the human iris using artificial neural networks.In Proceedings of the 11th Mediterranean Electrotechnical Conference,Cairo,Egypt,May 7-9,2002,5:580-583.
[88]严民军,齐翔林,汪九云.虹膜识别中的一种神经算法的研究.生物物理学报.2000,16(4):711-716
[89]Wang Y,Han J.his Recognition Using Support Vector Machines.Proceedings of International Symposium on Neural Networks,2004:622-628.
[90]古红英,潘红,吴飞,等.虹膜自相似性识别方法研究.计算机辅助设计与图形学学报,2004,16(7):973-977
[91]孟爱国,章登勇,陈志坚,等.基于小波包变换和支持向量机的虹膜识别方法.计算机工程与设计,2006,27(10):1769-1771,1774
[92]林金龙,石青云.一种基于结构特征的虹膜识别方法.计算机工程与应用.2003.13:83-84,137
[93]张鹏飞,李德胜,王祁.一种新颖的虹膜识别方法.光电工程,2005,32(5):89-92
[94]Mira J,Mayer J.Image Feature Extraction for Application of Biometric Identification of Iris:A Morphological Approach.Proceeding of the ⅩⅥ Brazilian Symposium on Computer Graphics And processing,2003:391-398
[95]陈颖,秦荫桐,韩方等.一种用于虹膜识别的相位匹配算法.上海大学学报.2002,8(1):31-34
[96]王春,叶虎年.虹膜识别算法研究.贵州工业大学学报.2000,29(3):48-52
[97]黄雅平,罗四维,陈恩义.基于独立分量分析的虹膜识别方法.计算机研究与发展.20003,40(10):1451-1454
[98]Huang Y.An Efficient Iris Recognition System.Proceedings of the First International Conference on Machine Learning and Cybernetics.2002,11:450-454
[99]Wang Y,Han J.Iris Recognition Using Independent Component Analysis.Machine Learning and Cybernetics,2005,7:4487-4492
[100]Noh S,Bae K,Park Y,et al.Multiresolution Independence Component Analysis Iris Identification.http://www.kmutt.ac.th/itc2002/CD/pdf/190745/FA1OC/4.pdf
[101]Wang C,Zhang D,Li Y.Iris verification based on fractional fourier transform.Proceedings of 2002 International Conference on Machine Learning and Cybernetics.2002,3:1470-1473
[102]Shang Y.A Novel Personal Biometric Authentication Technique Using Human Iris Based on Fractal Dimension Feature.ICASSP,2003:201-204
[103]殷珊珊.基于分形理论的虹膜识别算法研究.中国科技大学硕士学位论文.2004
[104]Noh S,Bae K,Park Y,et al.A Novel Method to Extract Feature for his Recognition System.AVBPA.2003:862-868
[105]Zhang P,Li D,Wang Q.A Novel his Recognition Method Based on Feature Fusion.2004 IEEE International Conference on Machine Learning and Cybernetics,2004,9:3661-3665
[106]何家峰,叶虎年,叶妙元.一种基于子块图像互相关的虹膜识别方法.计算机工程与应用.2003,7:63-64,131
[107]Lu C,Lu Z.Zemike moment invariant based iris recognition.In Proceedings of the 2004 Chinese Conference on Biometric Recognition,Guangzhou,China,December 13-14,2004,LNCS 3338:554-561.
[108]Yuan X,Shi P.his Feature Extraction Using 2D Phase Congruency.Information Technology and Applications,2005,ICITA 2005,2:437-441
[109]肖志涛,于明,李锵,等.Log Gabor小波性能分析及其在相位一致性中应用.天津大学学报,2003,36(4):443-446
[110]肖鹏峰,冯学智,赵书河,等.一种基于相位一致的高分辨率遥感图像特征检测方法.遥感学报,2007,11(3):303-310
[111]冯象初,甘小冰,宋国乡编.数值泛函与小波理论.西安:西安电子科技大学出版社,2003
[112]梁学章,何甲兴,王新民,等.小波分析.北京:国防工业出版社,2005
[113]成礼智,郭汉伟著.小波与离散变换理论及工程实践.北京:清华大学出版社,2005
[114]Mallat S G,Zhong S.Characterization of Signals from Multiscale Edge.IEEE T-PAM I,1992,14(7):710-732
[115]Mallat S G,Wang H.Singularity Detection and Processing with Wavelets IEEE Transaction on Information Theory.1992.38(2):617-643
[116]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2003
[117]Q.Chen.Image registration and its application in medical imaging.PhD Thesis,Vrije University Brussel,Faculty of Applied Seiences,Brussel,1998
[118]S.Marcelja.Mathematical description of the responses of simple cortical cells.J.Opt.Soc.Am,1980,70/11:1297-1300
[119]J.G.Daugman.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters.Journal of the Optical Society of America A,July 1985,2(7):1160-1169
[120]J.G.Daugman.Six Formal Properties of Two-Dimensional Anisotropic Visual Filters:Structural Principle and Frequency/Orientation Selectivity.IEEE Trans.on Man,and Cyberneticx,Cet.1989,Vol.SMC-13,No.5:882-887
[121]D.Hubel,T.Wiesel.Receptive fields,binocular interaction and functional architecture in the cat's visual cortex.J.Physiol.(London),1962,Vol:106-154
[122]J.A.Movshon,I.D.Thompson,D.J.Tolhurst.Spatial summation in the receptive fields of simple cells in the cat's striate cortex.J.Physiol.(London),1978,283:53-77
[123]D.Van Essen.Hierarchical organization and functional streams in the visual cortex.Annu.Rev.Neurosci.1979,Vol.2:227-263
[124]P.Kruizinga,N.Petkov,S.E.Grigorescu.Comparison of texture features based on Gabor filters.Proceedings of the 10th International Conference on Image Analysis and Processing,Venice,Italy,September 27-29,1999,142-147
[125]Simona E.Grigorescu,Nicolai Petkov,Peter Kruizinga.Comparison of Texture Features
[126]Based on Gabor Filters.IEEE TRANSACTIONS ON IMAGE PROCESSING,OCTOBER 2002,11(10):1160-1167
[127]Cosmin Grigorescu,Nicolai Petkov,Michel A.Westenberg.Contour Detection Based on Nonclassical Receptive Field Inhibition.IEEE TRANSACTIONS ON IMAGE PROCESSING,JULY 2003,12(7):729-739
[128]N.Petkov,P.Kruizinga.Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli:bar and grating cells.1997,83-96
[129]Peter Kruizinga,Nikolay Petkov.Nonlinear Operator for Oriented Texture.IEEE TRANSACTIONS ON IMAGE PROCESSING,OCTOBER 1999,8(10):1395-1407
[130]明星.虹膜识别技术中小波变换的应用原理与方法.吉林大学博士学位论文.2006