非接触定焦成像的掌纹图像识别算法研究
|
摘要
掌纹识别技术作为生物特征识别技术领域里的一员,以其丰富的信息量、稳定而唯一的特征、采集设备简单、受噪声影响小、用户接受程度高等特点,近年来受到了众多研究团队的重视。
图像采集是掌纹图像处理与识别的基础。由于接触式采集装置体积大,成本高,具有污渍、疾病传播的可能性以及信息的泄漏,使得非接触方式取代接触方式采集掌纹图像是发展的必然。然而每个人对于距离和方位的感觉存在差异,将导致非接触拍摄的掌纹图像存在缩放、变形、模糊、旋转和平移等问题,同时由于成像装置并不要求人手处于密闭的空间,致使光照不均,产生低对比度的图像,给掌纹的准确识别造成了极大的困难。
解决非接触定焦拍摄的掌纹图像中存在的各种问题,是掌纹识别研究的关键。本文以自采的手形图像库为实验基础,以准确提取掌纹特征为研究目标,以解决模糊、变形和缩放等现象的预处理算法和提高掌纹识别率的小波分解下的高低频信息处理方法为研究内容,构建了一套适用于非接触定焦拍摄的掌纹图像识别算法。具体来说,主要工作及贡献如下：
(1)提出利用小波的多分辨分析特性对掌纹图像进行小波分解,分解后的低频域的子图像继续高低帽变换,寻找图像中的灰度槽,降低了部分残存的模糊现象影响,提高了图像对比度；同时对小波分解的高频系数进行分块统计,削弱了部分变形和缩放现象的影响。而后借助于融合的思想,把小波分解下的低频域处理信息与高频域下的处理信息进行融合以构成特征向量,计算特征向量间的欧氏距离进行掌纹匹配。在自采的图库上进行实验,得到了90%的识别率,证明了算法的有效性。
(2)提出把Radon变换应用于测试掌纹ROI子图像和注册掌纹ROI子图像,从而获取最大能量对应的角度,两个角度的差值作为测试图像相对于注册图像的倾斜角度,然后利用空间几何坐标关系对非接触式成像系统引入的掌纹图像变形进行了纠正。相关系数评价指标证明此种方法是有效的。
(3)提出利用掌纹ROI的梯度图像寻找边界点集和局部梯度极大值点,通过设置阈值的方法在极大值附近寻找边界点,从而获取经过分析认定的圆形点扩展函数的模糊半径,进而获得退化模型,接着利用Lucy-Richardson方法对模糊掌纹图像进行复原,解决非接触式成像系统引入的离焦模糊问题。从处理的图像视觉效果以及所用的改进平方梯度评价指标数据来看,此种模糊图像复原方法是有效的。
(4)提出根据手腕、手掌和手指的宽度变化规律来确定手掌图像的有效性,以解决非接触拍摄掌纹图像时被测试者不能保证将手掌放置在同一中心位置或者足够的手指伸展程度,从而产生无效的手掌图像。对库内的500幅手掌图像进行测试,通过率达到98%。
(5)提出把注册掌纹图像与测试掌纹图像相对应的指根部1和3两点的距离比作为图像列放大系数,把指根点3到通过指根点4且平行通过1、3指根点直线的垂直距离比作为图像行放大系数,利用双线性插值方法对非接触定焦距采集的掌纹图像进行处理,解决了由于采样距离不同造成的掌纹图像大小不一致的问题。
As a biometric technology, the palmprint recognition has attracted many researchers because of its features such as rich information, stable and unique features, simple acquisition equipment, small noise effect and easy acceptance.
The image collection is the base of the palmprint image process and identification. Because the contact acquisition devices are large, high cost, full of besmirch, the possibility of the disease propagation and information leakage, it is inevitable that the contact palmprint images will be replaced by the non-contact palmprint images. However, everyone's sense is different for the distance and the orientation. It will cause the collecting palmprint images with the scaling, deformation, blur, rotation, translation and so on. At the same time, the images will be low contrast because the hands are not required to put the closed space by the imaging equipment, which leads the uneven light. It will make it more difficult to get the accurate palmprint recognition.
The key to the study for palmprint recognition is to solve all the problems existing in the non-contact fixed-focus imaging palmprint images. In this dissertation, a recognition algorithm suitable for non-contact fixed-focus palmprint images is constituted based on the palmprint base collected by us. Its object is to extract the palmprint features accurately. The research contents consist of the preprocessed algorithms which solves the questions on the blur, morphing, scaling and so on and the high and low-frequency information processing approaches based on wavelet transform which can increase the recognition rate of the palmprints. In detail, the main jobs and contributions are as follows:
(1) It proposes that the multi-resolution analysis characteristics about wavelet decomposition is used to decompose the palmprint image. The low frequency sub-images after decomposition is dealed with high and low hat transforms. It looks for the gray slot of the image so that the partly residual blur is reduced and the image contrast is highlighted. At the same time, the high frequency domain of the wavelet decomposition is carried block statistics so that the residual deformation and the scaling are reduced. At last by the fusion concept, the processed information about the low frequency domain under wavelet decomposition and the processed information about the high frequency domain under wavelet decomposition are fused to form the feature vector. The Euclidean distances between the feature vectors are calculated for the palmprint match. The recognition rate90%was got from the self-collecting image base. It proves the validity of the proposed algorithm.
(2) It is proposed that the angles corresponding to the maximum energy are got by Radon transform for the testing ROI subimage and the registration ROI subimage, respectively. The difference of two angles between the testing image and the registration image is used as the leaning angle. Then the space geometry coordinate relationship is used to correct the deformation of the palmprint image caused by the non-contact imaging system. The correlation coefficient index proves the method is effective.
(3) It proposes that the gradient image about palmprint ROI is used to find the boundary dots and the maximum gradient values in the local regions. The boundary dots are found near the maximum values by setting the thresholds. Then the radius of the round point spread function identified through analysis will be determined so that the degradation model is got. Then the Lucy-Richardson method is used to restore the fuzzy palmprint images to solve the defocus blurring question caused by the non-contact imaging system. According to the processed image visual effect and improved square gradient assessment index, the restoration method for the blurred images is effective.
(4) The method of determining the effectiveness of the palms is proposed according to the width change of the wrist, the palm and the fingers. When the contactless way is adopted to collect the images, it is hard to ensure that everyone will place the palm in the same center position every time or has enough finger stretching so that the inefficient palms occur. The passing rate was98%when500palm images in the palmprint base were tested.
(5) It is proposed that the corresponding distance ratio of the first and the third fingerdots between the registration palmprint image and the testing palmprint image is used as the column scaling factor. And the corresponding distance ratio through the third finger dot vertical to the line parallel to the line between the first and the third finger dots through the fourth finger dot is used as the row scaling factor. Then the bilinear interpolation method is adopted to process the palmprint images got by the non-contact fixed-focus acquisition. It solves some questions about the palmprint images with different sizes caused by different sampling distances.
图像采集是掌纹图像处理与识别的基础。由于接触式采集装置体积大,成本高,具有污渍、疾病传播的可能性以及信息的泄漏,使得非接触方式取代接触方式采集掌纹图像是发展的必然。然而每个人对于距离和方位的感觉存在差异,将导致非接触拍摄的掌纹图像存在缩放、变形、模糊、旋转和平移等问题,同时由于成像装置并不要求人手处于密闭的空间,致使光照不均,产生低对比度的图像,给掌纹的准确识别造成了极大的困难。
解决非接触定焦拍摄的掌纹图像中存在的各种问题,是掌纹识别研究的关键。本文以自采的手形图像库为实验基础,以准确提取掌纹特征为研究目标,以解决模糊、变形和缩放等现象的预处理算法和提高掌纹识别率的小波分解下的高低频信息处理方法为研究内容,构建了一套适用于非接触定焦拍摄的掌纹图像识别算法。具体来说,主要工作及贡献如下：
(1)提出利用小波的多分辨分析特性对掌纹图像进行小波分解,分解后的低频域的子图像继续高低帽变换,寻找图像中的灰度槽,降低了部分残存的模糊现象影响,提高了图像对比度；同时对小波分解的高频系数进行分块统计,削弱了部分变形和缩放现象的影响。而后借助于融合的思想,把小波分解下的低频域处理信息与高频域下的处理信息进行融合以构成特征向量,计算特征向量间的欧氏距离进行掌纹匹配。在自采的图库上进行实验,得到了90%的识别率,证明了算法的有效性。
(2)提出把Radon变换应用于测试掌纹ROI子图像和注册掌纹ROI子图像,从而获取最大能量对应的角度,两个角度的差值作为测试图像相对于注册图像的倾斜角度,然后利用空间几何坐标关系对非接触式成像系统引入的掌纹图像变形进行了纠正。相关系数评价指标证明此种方法是有效的。
(3)提出利用掌纹ROI的梯度图像寻找边界点集和局部梯度极大值点,通过设置阈值的方法在极大值附近寻找边界点,从而获取经过分析认定的圆形点扩展函数的模糊半径,进而获得退化模型,接着利用Lucy-Richardson方法对模糊掌纹图像进行复原,解决非接触式成像系统引入的离焦模糊问题。从处理的图像视觉效果以及所用的改进平方梯度评价指标数据来看,此种模糊图像复原方法是有效的。
(4)提出根据手腕、手掌和手指的宽度变化规律来确定手掌图像的有效性,以解决非接触拍摄掌纹图像时被测试者不能保证将手掌放置在同一中心位置或者足够的手指伸展程度,从而产生无效的手掌图像。对库内的500幅手掌图像进行测试,通过率达到98%。
(5)提出把注册掌纹图像与测试掌纹图像相对应的指根部1和3两点的距离比作为图像列放大系数,把指根点3到通过指根点4且平行通过1、3指根点直线的垂直距离比作为图像行放大系数,利用双线性插值方法对非接触定焦距采集的掌纹图像进行处理,解决了由于采样距离不同造成的掌纹图像大小不一致的问题。
As a biometric technology, the palmprint recognition has attracted many researchers because of its features such as rich information, stable and unique features, simple acquisition equipment, small noise effect and easy acceptance.
The image collection is the base of the palmprint image process and identification. Because the contact acquisition devices are large, high cost, full of besmirch, the possibility of the disease propagation and information leakage, it is inevitable that the contact palmprint images will be replaced by the non-contact palmprint images. However, everyone's sense is different for the distance and the orientation. It will cause the collecting palmprint images with the scaling, deformation, blur, rotation, translation and so on. At the same time, the images will be low contrast because the hands are not required to put the closed space by the imaging equipment, which leads the uneven light. It will make it more difficult to get the accurate palmprint recognition.
The key to the study for palmprint recognition is to solve all the problems existing in the non-contact fixed-focus imaging palmprint images. In this dissertation, a recognition algorithm suitable for non-contact fixed-focus palmprint images is constituted based on the palmprint base collected by us. Its object is to extract the palmprint features accurately. The research contents consist of the preprocessed algorithms which solves the questions on the blur, morphing, scaling and so on and the high and low-frequency information processing approaches based on wavelet transform which can increase the recognition rate of the palmprints. In detail, the main jobs and contributions are as follows:
(1) It proposes that the multi-resolution analysis characteristics about wavelet decomposition is used to decompose the palmprint image. The low frequency sub-images after decomposition is dealed with high and low hat transforms. It looks for the gray slot of the image so that the partly residual blur is reduced and the image contrast is highlighted. At the same time, the high frequency domain of the wavelet decomposition is carried block statistics so that the residual deformation and the scaling are reduced. At last by the fusion concept, the processed information about the low frequency domain under wavelet decomposition and the processed information about the high frequency domain under wavelet decomposition are fused to form the feature vector. The Euclidean distances between the feature vectors are calculated for the palmprint match. The recognition rate90%was got from the self-collecting image base. It proves the validity of the proposed algorithm.
(2) It is proposed that the angles corresponding to the maximum energy are got by Radon transform for the testing ROI subimage and the registration ROI subimage, respectively. The difference of two angles between the testing image and the registration image is used as the leaning angle. Then the space geometry coordinate relationship is used to correct the deformation of the palmprint image caused by the non-contact imaging system. The correlation coefficient index proves the method is effective.
(3) It proposes that the gradient image about palmprint ROI is used to find the boundary dots and the maximum gradient values in the local regions. The boundary dots are found near the maximum values by setting the thresholds. Then the radius of the round point spread function identified through analysis will be determined so that the degradation model is got. Then the Lucy-Richardson method is used to restore the fuzzy palmprint images to solve the defocus blurring question caused by the non-contact imaging system. According to the processed image visual effect and improved square gradient assessment index, the restoration method for the blurred images is effective.
(4) The method of determining the effectiveness of the palms is proposed according to the width change of the wrist, the palm and the fingers. When the contactless way is adopted to collect the images, it is hard to ensure that everyone will place the palm in the same center position every time or has enough finger stretching so that the inefficient palms occur. The passing rate was98%when500palm images in the palmprint base were tested.
(5) It is proposed that the corresponding distance ratio of the first and the third fingerdots between the registration palmprint image and the testing palmprint image is used as the column scaling factor. And the corresponding distance ratio through the third finger dot vertical to the line parallel to the line between the first and the third finger dots through the fourth finger dot is used as the row scaling factor. Then the bilinear interpolation method is adopted to process the palmprint images got by the non-contact fixed-focus acquisition. It solves some questions about the palmprint images with different sizes caused by different sampling distances.
引文
[1]Miller B. Vital signs of identity. IEEE Spectrum,1994,31(2):22-30.
[2]Jain A K, Hong L, Pankanti S. Biometrics:Promising frontiers for emerging identification market. Computer,2000,33(2):91-98.
[3]Zhang D. Automated biometrics:technologies and systems. USA:Kluwer Academic Publishers, 2002.
[4]苑玮琦,柯丽,白云.生物特征识别技术.北京：科学出版社,2009.
[5]Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuit and System for Video Technology,2004,14(1):4-20.
[6]Prabhakar S, Pankanti S, Jain A K. Biometric recognition:security and privacy concerns. IEEE Security & Privacy,2003,1(3):45-49.
[7]袁国武,魏骁勇,徐丹.基于掌纹的身份鉴别.计算机辅助设计与图形学学报,2005,17(12)：2590-2595.
[8]PolyU Palmprint Database. http://www4.comppolyu.edu.hk/-biometrics/.2003-03-02.
[9]HELEN. UST Hand Image Database [DB/OL]. http://visgraph.cs.ust.hk/biometrics/Visgraph web/index.html.2003.
[10]Paul S W, Li M. Pyramid adaptive dynamic Hough transform to detect edges with arbitrary shapes. Optical Engineering,1997,36(5):1431-1437.
[11]Shu W, Zhang D. Automated personal identification by palmprint. Optical Engineering,1998, 37(8):2359-2362.
[12]Shu W, Zhang D. Palmprint verification:an implementation of biometric technology. IEEE international conference on pattern recognition,1998,1:219-221.
[13]Fan L, Sung K K.A combined feature-texture similarity measure for face alignment under varying pose. IEEE Conference on Computer Vision and Pattern Recognition, South Carolina,2000: 308-313.
[14]黎明,严超华.基于掌纹图像分析的自动识别系统.中国图像图形学报.2000,5(2)134-137.
[15]Duta N, Jain A, Mardia K. Matching of palmprint. Pattern Recognition Letters,2001, 23(4):477-485.
[16]张泽,束为,荣钢.基于乳突文方向特性的掌纹自动分类方法.清华大学学报(自然科学版),2002,42(9)：1222-1224.
[17]Han C C, Cheng H L, Lin C L. Personal authentication using palmprint features. Pattern Recognition,2003,36(2):371-381.
[18]孙佳,彭玉华.基于模板匹配的掌纹识别自适应阈值选取方法.山东大学学报(工学版),2005,35(5)：73-77.
[19]Jia W, Ling B, Chau K W, et al. Palmprint identification using restricted fusion. Applied Mathematics and Computation,2008,205(2):927-934.
[20]Jia W, Huang D S, Zhang D. Palmprint verification based on robust line orientation code. Pattern Recognition,2008,41(5):1504-1513.
[21]Zhang D, Lu G, Li W, et al. Palmprint recognition using 3-D information. IEEE Transactions on Systems, Man and Cybernetics-Part C:Applications and Reviews,2009,39(5):505-519.
[22]Li W, Zhang D, Zhang L, et al.3-D Palmprint recognition with joint line and orientation features. IEEE Transactions on Systems, Man and Cybernetics-Part C:Applications and Reviews,2011, 41(2):274-279.
[23]Zhang D, Guo Z H, Lu G M, et al. Online joint palmprint and palmvein verification. Expert Systems with Applications,2011,38(3):2621-2631.
[24]Li W X, Zhang D, Xu Z Q. Palmprint recognition based on Fourier transform. Journal of Software, 2002,13(5):879-886.
[25]Zhang D, Kong W K, You J, et al.. Online palmprint identification.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[26]黄申,屈景辉,卢虹冰.采样式多通道Gabor滤波对掌纹的特征提取和分类.中国图象图形学报,2007,12(12)：2148-2157.
[27]Zhang D, Guo Z H, Lu G M, et al. An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement,2010,59 (2):480-490.
[28]Li W, Zhang D, You J, et al. A texture-based dynamic selection scheme for palmprint identification. Proceeding of 13th International Conference on Computer Application in Industry and Engineering, 2000:110-113.
[29]吴介,裘正定,李强.一种新的掌纹特征提取算法.北京交通大学学报,2006,30(2)：89-100.
[30]赵静岚,庄镇泉.基于log-Gabor与FT数据融合的掌纹识别算法.电路与系统学报,2010,15(2)：17-21.
[31]吴双元,王宽全.基于改进的广义K—-L变换的掌纹识别.哈尔滨商业大学学报(自然科学版),2004,20(6)：659-665.
[32]郭金玉,苑玮琦.基于小波变换和部分最小二乘的掌纹识别.光电子·激光,2008,19(4)：554-557.
[33]Pan X, Ruan Q Q. Palmprint recognition with improved two-dimensional locality preserving projections. Image and Vision Computing,2008,26(9):1261-1268.
[34]吴旭宾,汪同庆,李宏友等.一种新的掌纹特征提取方法研究.计算机应用研究,2009,26(1)：398-400.
[35]郭金玉,李元,孔晓光等.基于局部保持投影和核直接判别分析的掌纹识别.光电子·激光,2011,22(1)：127-130.
[36]尚丽,苏品刚,杜吉祥.基于局部非负稀疏编码的掌纹识别方法.计算机应用,2011,31(6)：1609-1612.
[37]Methani C, Namboodiri A M. Pose invariant palmprint recognition.3rd International Conference on Biometrics, Alghero, Italy,2009:577-586.
[38]Goh M, Connie T, Teoh A. Touch-less palmprint biometrics:Novel design and implementation. Image and Vision Computing,2008,26:1551-1560.
[39]练秋生,刘春亮.基于Gabor滤波器和LBP的分级掌纹识别.计算机工程与应用,2007,43(6)：211-215.
[40]Butt M A A. Masood H, Mumtaz M, et al. Palmprint identification using contourlet transform.2nd IEEE International Conference on Biometrics:Theory, Applications and Systems, Arlington, VA, 2008:1-5.
[41]杨震群.基于局部兴趣点和弹性图匹配的掌纹识别技术研究：(硕士学位论文).昆明：云南师范大学,2007.
[42]郑凯元.针对掌纹图像的配准与融合方法研究：(硕士学位论文).长春：东北师范大学,2008.
[43]Sang H F, Liu F. Defocused palmprint recognition using 2DPCA. International Conference on Artificial Intelligence and Computational Intelligence,2009:611-615.
[44]刘芳.离焦成像状态下掌纹特征提取方法的研究：(硕士学位论文).沈阳：沈阳工业大学,2009.
[45]Methani C, Namboodiri A M. Video Based Palmprint Recognition.2010 International Conference on Pattern Recognition, Istanbul,2010:1352-1355.
[46]Kanhangad V, Kumar A, Zhang D. Contactless and pose invariant biometric identification using hand surface. IEEE Transactions on Image Processing,2011,20(5):1415-1424.
[47]林森,苑玮琦.基于Demons算法的变形掌纹归一化方法研究.仪器仪表学报,2012,33(1)：62-68.
[48]http://www.imageedu.com/Mode/Equipment/Xuangou/200504/2649.html
[49]Laadjel M, Kurugollu F, Bouridane A, et al. Degraded partial palmprint recognition for forensic investigations.2009 IEEE International Conference on Image Processing, Cairo, Egypt,2009: 1513-1516.
[50]Lin C L, Chuang T C, Fan K C. Palmprint verification using hierarchical decomposition. Pattern Recognition,2005,38(12):2639-2652.
[51]徐飞,施晓红等Matlab应用图像处理.西安：西安电子科技大学出版社,2002.
[52]蒋如意,刘洪涛,胡文等.基于离焦图像模糊分析的微装配力测量.上海交通大学学报,2010,44(8)：1046-1050.
[53]沈满德,张敏,张法全等.离焦成像对面积测量的影响及误差修正.光子学报,2009,38(5)：1240-1244.
[54]李征,苏理云,马洪等.基于神经网络和维纳滤波的半盲离焦图像复原.激光杂志,2007.28(2)：39-40.
[55]孙辉,张葆,刘晶红等.离焦模糊图像的维纳滤波恢复.光学技术.2009,35(2)：295-297.
[56]胡小平,陈国良,毛征宇等.离焦模糊图像的维纳滤波复原研究.仪器仪表学报,2007,28(3)：479-482.
[57]William H. Richardson. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America,1972,62(1):55-59.
[58]L.B.Lucy. An iterative technique for the rectification of ovserved images. The Astronomical Journal,1974,79(6):745-754.
[59]David S. C. Biggs, Mark Andrews. Acceleration of iterative image restoration algorithms. Applied Optics,1997,36(8):1766-1775.
[60]Shepp L A, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging,1982,1(2):113-122.
[61]赵琳,金伟其,陈翼男等.一种新的运动模糊图像超分辨力盲复原算法.光子学报,2008,37(11)：2355-2359.
[62]胡小平,尹喜云,陈国良等.离焦模糊图像的自适应滤波及逆滤波器复原.光电工程,2007,34(4)：105-108.
[63]王正友,伍世虔,徐升华等.一种离焦模糊图像客观检测的新方法.中国图像图形学报,2007,12(6)：1008-1013.
[64]孙艳忠,柴毅,尹宏鹏.基于边缘检测与改进最大类间方差法的火箭序列图像分割算法.计算机应用,2009,29(11)：3027-3032.
[65]谢小甫,周进,吴钦章.一种针对图像模糊的无参考质量评价指标.计算机应用,2010,30(4)：921-924.
[66]申勤.数字图像清晰度评价函数的研究与改进.微型机与应用,2011,30(1)： 32-37.
[67]陈粟,倪林.一种人脸图像局部变形技术.计算机工程与应用,33,56-58.
[68]郎锐.数字图像处理学Visual C++实现.北京：北京希望电子出版社,2003.
[69]Wolverg G. Digital image warping. Los Alamitos, CA:IEEE Computer Society Press,1990.
[70]Sederberg T, Parry S. Free-form deformation of solid geometric models. Computer Graphics,1986, 20(4):151-160.
[71]Hsu W, Hughes J, Kaufman H. Direct manipulation of free-form deformation. Computer Graphics, 1992,16(2):177-184.
[72]Lee S, Chwa K, Hahn J, et al. Image morphing using deformation techniques. Journal of Visualization and Computer Animation,1996,7(1):3-23.
[73]Lee S, Wolberg G, Chwa K, et al. Image metamorphosis with scattered feature constraints. IEEE Transactions on Visualization and Computer Graphics,1996,2(4):337-354.
[74]Beier T, Neely S. Feature-based image metamorphosis. Computer Graphics,1992,26(2):35-42.
[75]Arad N, Reisfeld D. Image warping using few anchor points and radial functions. Computer Graphics Forum,1995,14(1):35-46.
[76]Ruprecht D, Mueller H. Image warping with scattered data interpolation. IEEE Computer Graphics and Applications,1995:15(2):37-43.
[77]Farial Shahnaz, Michael W. Berry, V. Paul Pauca, et al. Document clustering using nonnegative matrix factorization. Information Processing and Management,2006,42(2):373-386.
[78]田学东.光学公式识别技术研究：(博士学位论文).保定：河北大学,2007.
[79]张晓燕,尤红建,付琨.基于Radon变换的星载SAR图像窄V尾迹检测.测绘科学,2009,34(2)：117-118.
[80]黄中,徐成,屈景辉等.基于小波理论对掌纹主线提取和修复.中国图象图形学报,2006,11(8)：1139-1149.
[81]周晓.基于Randon变换和图像矩的抗RST攻击算法.计算机科学,2007,34(12)：257-259.
[82]张伟,童创明,张群.基于ATI技术和Radon变换的振动目标微多普勒提取.电子学报,2011,39(10)：2431-2435.
[83]刘新亮,李星野.基于Radon变换的多尺度虹膜特征提取算法.光电子·激光,2008,19(4)：532-536.
[84]甘俊英,何思斌.非线性Radon变换及其在人脸识别中的应用.模式识别与人工智能,2011,24(3)：405-410.
[85]Gui P L, Li J H, Pan Q, et al. Rotation and scaling invariant texture classification based on Radon transform and multi-scale analysis. Pattern Recognition,2006,27(5):408-413.
[86]张前进,徐素莉,孙炎增.基于 Radon变换特征提取的步态识别.数据采集与处理,2010,25(2)：268-272.
[87]杜杨洲.评价基于统计学习的人脸图像合成方法研究：(博士学位论文).北京：清华大学,2004.
[88]周景超,戴汝为,肖柏华.图像质量评价研究综述.计算机科学,2008,35(7)：1-8.
[89]苏本跃,盛敏.自适应图像缩放的切触有理混合插值算法.计算机工程与应用,2010,46(1)：196-199.
[90]Li W X, Zhang D, Xu Z Q. Image alignment based on invariant features for palmprint identification. Signal Process:Image Communication,2003,18 (5):373-379
[91]. Zhang D, Kong W K, You J, et al. Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[92]陈燕新,戚飞虎.一种新的提取轮廓特征点的方法.红外与毫米波学报.1998,17(3)：171-176.
[93]Kumar A, Wong D C M, Shen H C, et al.. Personal verification using palmprint and hand geometry biometric. Proceedings of the 4th international conference on Audio- and video-based biometric person authentication,2003:668-678.
[94]Zhang D. Palmprint authentication. Boston, USA:Kluwer Academic Publishers,2004:78-79.
[95]Liambas C, Tsouros C. An algorithm for detecting hand orientation and palmprint location from a highly noisy image. Proceedings of IEEE International Symposium on Intelligent Signal Processing. Alcala De Henares,Spain:IEEE,2007,1-6.
[96]Kim J K, Kim L S. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Video Technology,2001,11(4): 475-484.
[97]Chang D C, Wu W R. Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Transactions on Medical imaging,1998,17(4):518-531.
[98]于建宇,张峰,周献中等.利用小波变换和K均值聚类实现字幕区域分割.计算机辅助设计与图形学学报,2006,18(10)：1508-1512.
[99]王丽荣.基于小波变换的目标检测方法研究：(博士学位论文).长春：吉林大学,2006.
[100]宋怀波.低质量可见光图像的处理技术和识别方法研究：(博士学位论文).济南：山东大学,2009.
[101]Prevost L, Oudot L, Moises A, et al. Hybrid generative/discriminative classifier for unconstrained character recognition. Pattern Recognition Letters,2005,26(12):1840-1848.
[102]Soille P. Morphological image analysis principles and applications. New York:Springer Verlag Inc,1999:236-245.
[103]Park H, Yoo J. Structuring element decomposition for efficient implementation of morphological filters. IEEE Image Signal Process,2001,148(1):31-35.
[104]白军.医学x胸片疑似肺部节点图像增强技术的研究：(硕士学位论文).西安：西安理工大学,2010.
[105]吴介,裘正定,孙冬梅.一种基于小波变换和不变矩的掌纹特征提取方法.第四届全国信号与信息处理联合学术会议,张家界,2005：191-195.
[106]周新虹,彭玉华,薛玉利等.应用小波变换和支持向量机的掌纹识别.计算机工程与应用,2007,43(22)：238-240.
[107]郭金玉,苑玮琦.基于独立成分分析的掌纹识别.光电工程,2008,35(3)：136-139.
[108]郭金玉,苑玮琦.基于局部保持投影的掌纹识别.光学学报,2008,28(10)：1920-1924.
[109]陈才扣,黄璞.基于分块独立分量分析的人脸识别.中国图像图形学报,2009,14(9)：1837-1842.
[110]苏晓生,林喜荣,丁天怀等.基于小波变换的掌纹特征提取.清华大学学报(自然科学版),2003,43(8)：1049-1055.
[111]Burt P J, Adelson E H. The Laplacian pyramid as a compact image code. IEEE Trans. on Communications,1983,31(4):532-540.
[112]Mallat S. A theory for multiresolution signal decomposition:the wavelet representation. IEEE Trans on PAMI,1989,11(7):674-693.
[113]刘春亮.掌纹图像在身份识别和认证中的应用：(硕士学位论文).秦皇岛：燕山大学,2007.
[114]焦竹青.变换域中的多源图像融合方法研究：(博士学位论文).无锡：江南大学,2011.
[115]李晖晖.多传感器图像融合算法研究：(博士学位论文).西安：西北工业大学,2006.
[116]Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing,1995,27(3):235-244.
[117]Chipman L J, Orr T M. Wavelets and images fusion. Proceedings of IEEE International Conference on Image processing, Washington D. C., USA,1995:248-251.
[2]Jain A K, Hong L, Pankanti S. Biometrics:Promising frontiers for emerging identification market. Computer,2000,33(2):91-98.
[3]Zhang D. Automated biometrics:technologies and systems. USA:Kluwer Academic Publishers, 2002.
[4]苑玮琦,柯丽,白云.生物特征识别技术.北京：科学出版社,2009.
[5]Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuit and System for Video Technology,2004,14(1):4-20.
[6]Prabhakar S, Pankanti S, Jain A K. Biometric recognition:security and privacy concerns. IEEE Security & Privacy,2003,1(3):45-49.
[7]袁国武,魏骁勇,徐丹.基于掌纹的身份鉴别.计算机辅助设计与图形学学报,2005,17(12)：2590-2595.
[8]PolyU Palmprint Database. http://www4.comppolyu.edu.hk/-biometrics/.2003-03-02.
[9]HELEN. UST Hand Image Database [DB/OL]. http://visgraph.cs.ust.hk/biometrics/Visgraph web/index.html.2003.
[10]Paul S W, Li M. Pyramid adaptive dynamic Hough transform to detect edges with arbitrary shapes. Optical Engineering,1997,36(5):1431-1437.
[11]Shu W, Zhang D. Automated personal identification by palmprint. Optical Engineering,1998, 37(8):2359-2362.
[12]Shu W, Zhang D. Palmprint verification:an implementation of biometric technology. IEEE international conference on pattern recognition,1998,1:219-221.
[13]Fan L, Sung K K.A combined feature-texture similarity measure for face alignment under varying pose. IEEE Conference on Computer Vision and Pattern Recognition, South Carolina,2000: 308-313.
[14]黎明,严超华.基于掌纹图像分析的自动识别系统.中国图像图形学报.2000,5(2)134-137.
[15]Duta N, Jain A, Mardia K. Matching of palmprint. Pattern Recognition Letters,2001, 23(4):477-485.
[16]张泽,束为,荣钢.基于乳突文方向特性的掌纹自动分类方法.清华大学学报(自然科学版),2002,42(9)：1222-1224.
[17]Han C C, Cheng H L, Lin C L. Personal authentication using palmprint features. Pattern Recognition,2003,36(2):371-381.
[18]孙佳,彭玉华.基于模板匹配的掌纹识别自适应阈值选取方法.山东大学学报(工学版),2005,35(5)：73-77.
[19]Jia W, Ling B, Chau K W, et al. Palmprint identification using restricted fusion. Applied Mathematics and Computation,2008,205(2):927-934.
[20]Jia W, Huang D S, Zhang D. Palmprint verification based on robust line orientation code. Pattern Recognition,2008,41(5):1504-1513.
[21]Zhang D, Lu G, Li W, et al. Palmprint recognition using 3-D information. IEEE Transactions on Systems, Man and Cybernetics-Part C:Applications and Reviews,2009,39(5):505-519.
[22]Li W, Zhang D, Zhang L, et al.3-D Palmprint recognition with joint line and orientation features. IEEE Transactions on Systems, Man and Cybernetics-Part C:Applications and Reviews,2011, 41(2):274-279.
[23]Zhang D, Guo Z H, Lu G M, et al. Online joint palmprint and palmvein verification. Expert Systems with Applications,2011,38(3):2621-2631.
[24]Li W X, Zhang D, Xu Z Q. Palmprint recognition based on Fourier transform. Journal of Software, 2002,13(5):879-886.
[25]Zhang D, Kong W K, You J, et al.. Online palmprint identification.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[26]黄申,屈景辉,卢虹冰.采样式多通道Gabor滤波对掌纹的特征提取和分类.中国图象图形学报,2007,12(12)：2148-2157.
[27]Zhang D, Guo Z H, Lu G M, et al. An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement,2010,59 (2):480-490.
[28]Li W, Zhang D, You J, et al. A texture-based dynamic selection scheme for palmprint identification. Proceeding of 13th International Conference on Computer Application in Industry and Engineering, 2000:110-113.
[29]吴介,裘正定,李强.一种新的掌纹特征提取算法.北京交通大学学报,2006,30(2)：89-100.
[30]赵静岚,庄镇泉.基于log-Gabor与FT数据融合的掌纹识别算法.电路与系统学报,2010,15(2)：17-21.
[31]吴双元,王宽全.基于改进的广义K—-L变换的掌纹识别.哈尔滨商业大学学报(自然科学版),2004,20(6)：659-665.
[32]郭金玉,苑玮琦.基于小波变换和部分最小二乘的掌纹识别.光电子·激光,2008,19(4)：554-557.
[33]Pan X, Ruan Q Q. Palmprint recognition with improved two-dimensional locality preserving projections. Image and Vision Computing,2008,26(9):1261-1268.
[34]吴旭宾,汪同庆,李宏友等.一种新的掌纹特征提取方法研究.计算机应用研究,2009,26(1)：398-400.
[35]郭金玉,李元,孔晓光等.基于局部保持投影和核直接判别分析的掌纹识别.光电子·激光,2011,22(1)：127-130.
[36]尚丽,苏品刚,杜吉祥.基于局部非负稀疏编码的掌纹识别方法.计算机应用,2011,31(6)：1609-1612.
[37]Methani C, Namboodiri A M. Pose invariant palmprint recognition.3rd International Conference on Biometrics, Alghero, Italy,2009:577-586.
[38]Goh M, Connie T, Teoh A. Touch-less palmprint biometrics:Novel design and implementation. Image and Vision Computing,2008,26:1551-1560.
[39]练秋生,刘春亮.基于Gabor滤波器和LBP的分级掌纹识别.计算机工程与应用,2007,43(6)：211-215.
[40]Butt M A A. Masood H, Mumtaz M, et al. Palmprint identification using contourlet transform.2nd IEEE International Conference on Biometrics:Theory, Applications and Systems, Arlington, VA, 2008:1-5.
[41]杨震群.基于局部兴趣点和弹性图匹配的掌纹识别技术研究：(硕士学位论文).昆明：云南师范大学,2007.
[42]郑凯元.针对掌纹图像的配准与融合方法研究：(硕士学位论文).长春：东北师范大学,2008.
[43]Sang H F, Liu F. Defocused palmprint recognition using 2DPCA. International Conference on Artificial Intelligence and Computational Intelligence,2009:611-615.
[44]刘芳.离焦成像状态下掌纹特征提取方法的研究：(硕士学位论文).沈阳：沈阳工业大学,2009.
[45]Methani C, Namboodiri A M. Video Based Palmprint Recognition.2010 International Conference on Pattern Recognition, Istanbul,2010:1352-1355.
[46]Kanhangad V, Kumar A, Zhang D. Contactless and pose invariant biometric identification using hand surface. IEEE Transactions on Image Processing,2011,20(5):1415-1424.
[47]林森,苑玮琦.基于Demons算法的变形掌纹归一化方法研究.仪器仪表学报,2012,33(1)：62-68.
[48]http://www.imageedu.com/Mode/Equipment/Xuangou/200504/2649.html
[49]Laadjel M, Kurugollu F, Bouridane A, et al. Degraded partial palmprint recognition for forensic investigations.2009 IEEE International Conference on Image Processing, Cairo, Egypt,2009: 1513-1516.
[50]Lin C L, Chuang T C, Fan K C. Palmprint verification using hierarchical decomposition. Pattern Recognition,2005,38(12):2639-2652.
[51]徐飞,施晓红等Matlab应用图像处理.西安：西安电子科技大学出版社,2002.
[52]蒋如意,刘洪涛,胡文等.基于离焦图像模糊分析的微装配力测量.上海交通大学学报,2010,44(8)：1046-1050.
[53]沈满德,张敏,张法全等.离焦成像对面积测量的影响及误差修正.光子学报,2009,38(5)：1240-1244.
[54]李征,苏理云,马洪等.基于神经网络和维纳滤波的半盲离焦图像复原.激光杂志,2007.28(2)：39-40.
[55]孙辉,张葆,刘晶红等.离焦模糊图像的维纳滤波恢复.光学技术.2009,35(2)：295-297.
[56]胡小平,陈国良,毛征宇等.离焦模糊图像的维纳滤波复原研究.仪器仪表学报,2007,28(3)：479-482.
[57]William H. Richardson. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America,1972,62(1):55-59.
[58]L.B.Lucy. An iterative technique for the rectification of ovserved images. The Astronomical Journal,1974,79(6):745-754.
[59]David S. C. Biggs, Mark Andrews. Acceleration of iterative image restoration algorithms. Applied Optics,1997,36(8):1766-1775.
[60]Shepp L A, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging,1982,1(2):113-122.
[61]赵琳,金伟其,陈翼男等.一种新的运动模糊图像超分辨力盲复原算法.光子学报,2008,37(11)：2355-2359.
[62]胡小平,尹喜云,陈国良等.离焦模糊图像的自适应滤波及逆滤波器复原.光电工程,2007,34(4)：105-108.
[63]王正友,伍世虔,徐升华等.一种离焦模糊图像客观检测的新方法.中国图像图形学报,2007,12(6)：1008-1013.
[64]孙艳忠,柴毅,尹宏鹏.基于边缘检测与改进最大类间方差法的火箭序列图像分割算法.计算机应用,2009,29(11)：3027-3032.
[65]谢小甫,周进,吴钦章.一种针对图像模糊的无参考质量评价指标.计算机应用,2010,30(4)：921-924.
[66]申勤.数字图像清晰度评价函数的研究与改进.微型机与应用,2011,30(1)： 32-37.
[67]陈粟,倪林.一种人脸图像局部变形技术.计算机工程与应用,33,56-58.
[68]郎锐.数字图像处理学Visual C++实现.北京：北京希望电子出版社,2003.
[69]Wolverg G. Digital image warping. Los Alamitos, CA:IEEE Computer Society Press,1990.
[70]Sederberg T, Parry S. Free-form deformation of solid geometric models. Computer Graphics,1986, 20(4):151-160.
[71]Hsu W, Hughes J, Kaufman H. Direct manipulation of free-form deformation. Computer Graphics, 1992,16(2):177-184.
[72]Lee S, Chwa K, Hahn J, et al. Image morphing using deformation techniques. Journal of Visualization and Computer Animation,1996,7(1):3-23.
[73]Lee S, Wolberg G, Chwa K, et al. Image metamorphosis with scattered feature constraints. IEEE Transactions on Visualization and Computer Graphics,1996,2(4):337-354.
[74]Beier T, Neely S. Feature-based image metamorphosis. Computer Graphics,1992,26(2):35-42.
[75]Arad N, Reisfeld D. Image warping using few anchor points and radial functions. Computer Graphics Forum,1995,14(1):35-46.
[76]Ruprecht D, Mueller H. Image warping with scattered data interpolation. IEEE Computer Graphics and Applications,1995:15(2):37-43.
[77]Farial Shahnaz, Michael W. Berry, V. Paul Pauca, et al. Document clustering using nonnegative matrix factorization. Information Processing and Management,2006,42(2):373-386.
[78]田学东.光学公式识别技术研究：(博士学位论文).保定：河北大学,2007.
[79]张晓燕,尤红建,付琨.基于Radon变换的星载SAR图像窄V尾迹检测.测绘科学,2009,34(2)：117-118.
[80]黄中,徐成,屈景辉等.基于小波理论对掌纹主线提取和修复.中国图象图形学报,2006,11(8)：1139-1149.
[81]周晓.基于Randon变换和图像矩的抗RST攻击算法.计算机科学,2007,34(12)：257-259.
[82]张伟,童创明,张群.基于ATI技术和Radon变换的振动目标微多普勒提取.电子学报,2011,39(10)：2431-2435.
[83]刘新亮,李星野.基于Radon变换的多尺度虹膜特征提取算法.光电子·激光,2008,19(4)：532-536.
[84]甘俊英,何思斌.非线性Radon变换及其在人脸识别中的应用.模式识别与人工智能,2011,24(3)：405-410.
[85]Gui P L, Li J H, Pan Q, et al. Rotation and scaling invariant texture classification based on Radon transform and multi-scale analysis. Pattern Recognition,2006,27(5):408-413.
[86]张前进,徐素莉,孙炎增.基于 Radon变换特征提取的步态识别.数据采集与处理,2010,25(2)：268-272.
[87]杜杨洲.评价基于统计学习的人脸图像合成方法研究：(博士学位论文).北京：清华大学,2004.
[88]周景超,戴汝为,肖柏华.图像质量评价研究综述.计算机科学,2008,35(7)：1-8.
[89]苏本跃,盛敏.自适应图像缩放的切触有理混合插值算法.计算机工程与应用,2010,46(1)：196-199.
[90]Li W X, Zhang D, Xu Z Q. Image alignment based on invariant features for palmprint identification. Signal Process:Image Communication,2003,18 (5):373-379
[91]. Zhang D, Kong W K, You J, et al. Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[92]陈燕新,戚飞虎.一种新的提取轮廓特征点的方法.红外与毫米波学报.1998,17(3)：171-176.
[93]Kumar A, Wong D C M, Shen H C, et al.. Personal verification using palmprint and hand geometry biometric. Proceedings of the 4th international conference on Audio- and video-based biometric person authentication,2003:668-678.
[94]Zhang D. Palmprint authentication. Boston, USA:Kluwer Academic Publishers,2004:78-79.
[95]Liambas C, Tsouros C. An algorithm for detecting hand orientation and palmprint location from a highly noisy image. Proceedings of IEEE International Symposium on Intelligent Signal Processing. Alcala De Henares,Spain:IEEE,2007,1-6.
[96]Kim J K, Kim L S. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Video Technology,2001,11(4): 475-484.
[97]Chang D C, Wu W R. Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Transactions on Medical imaging,1998,17(4):518-531.
[98]于建宇,张峰,周献中等.利用小波变换和K均值聚类实现字幕区域分割.计算机辅助设计与图形学学报,2006,18(10)：1508-1512.
[99]王丽荣.基于小波变换的目标检测方法研究：(博士学位论文).长春：吉林大学,2006.
[100]宋怀波.低质量可见光图像的处理技术和识别方法研究：(博士学位论文).济南：山东大学,2009.
[101]Prevost L, Oudot L, Moises A, et al. Hybrid generative/discriminative classifier for unconstrained character recognition. Pattern Recognition Letters,2005,26(12):1840-1848.
[102]Soille P. Morphological image analysis principles and applications. New York:Springer Verlag Inc,1999:236-245.
[103]Park H, Yoo J. Structuring element decomposition for efficient implementation of morphological filters. IEEE Image Signal Process,2001,148(1):31-35.
[104]白军.医学x胸片疑似肺部节点图像增强技术的研究：(硕士学位论文).西安：西安理工大学,2010.
[105]吴介,裘正定,孙冬梅.一种基于小波变换和不变矩的掌纹特征提取方法.第四届全国信号与信息处理联合学术会议,张家界,2005：191-195.
[106]周新虹,彭玉华,薛玉利等.应用小波变换和支持向量机的掌纹识别.计算机工程与应用,2007,43(22)：238-240.
[107]郭金玉,苑玮琦.基于独立成分分析的掌纹识别.光电工程,2008,35(3)：136-139.
[108]郭金玉,苑玮琦.基于局部保持投影的掌纹识别.光学学报,2008,28(10)：1920-1924.
[109]陈才扣,黄璞.基于分块独立分量分析的人脸识别.中国图像图形学报,2009,14(9)：1837-1842.
[110]苏晓生,林喜荣,丁天怀等.基于小波变换的掌纹特征提取.清华大学学报(自然科学版),2003,43(8)：1049-1055.
[111]Burt P J, Adelson E H. The Laplacian pyramid as a compact image code. IEEE Trans. on Communications,1983,31(4):532-540.
[112]Mallat S. A theory for multiresolution signal decomposition:the wavelet representation. IEEE Trans on PAMI,1989,11(7):674-693.
[113]刘春亮.掌纹图像在身份识别和认证中的应用：(硕士学位论文).秦皇岛：燕山大学,2007.
[114]焦竹青.变换域中的多源图像融合方法研究：(博士学位论文).无锡：江南大学,2011.
[115]李晖晖.多传感器图像融合算法研究：(博士学位论文).西安：西北工业大学,2006.
[116]Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing,1995,27(3):235-244.
[117]Chipman L J, Orr T M. Wavelets and images fusion. Proceedings of IEEE International Conference on Image processing, Washington D. C., USA,1995:248-251.