自动掌纹识别理论和算法研究
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摘要
基于生物特征识别的身份鉴别技术提供了一种高可靠性、高稳定性的身份鉴别方式。掌纹识别是一种相对较新的生物特征识别技术,但其发展非常迅速,现已成为生物特征识别技术领域中重要一员。研究掌纹识别技术具有重要的理论意义和巨大的应用价值,它涉及计算机视觉,模式识别,图像处理等多门学科,掌纹识别技术的研究有助于这些学科的发展;在信息安全、公共安全、法律等领域,掌纹识别技术均具有潜在的应用前景。目前尽管掌纹识别技术已取得了很大进展,但在实际应用中仍然存在许多问题亟待解决。本文对掌纹识别相关理论和算法进行了深入研究,主要工作和贡献如下:
(1)提出了一种基于Log-Gabor小波相位一致的掌纹线特征提取方法。掌纹线特征是掌纹识别中最基本、最直观的特征,但由于掌纹线的特殊性和复杂性,如何有效提取掌纹线特征一直是掌纹识别的难点。本文从频率域角度考虑,使用Log-Gabor小波相位一致方法提取掌纹图像线特征,包括掌纹方向相位一致特征和掌纹整体相位一致特征。该方法提取的线特征可有效包含纹线的结构信息、强度信息和宽度信息,且提取的线特征比较稳定。在掌纹整体相位一致特征图像上进一步检测出具体的掌纹线,仅使用掌纹线结构特征来表示和识别掌纹,实验结果验证了Log-Gabor小波相位一致提取掌纹线特征用于识别的有效性。
(2)提出了基于区域方向码的掌纹线特征表示方法。该方法首先将6个方向的掌纹方向相位一致特征图像均匀分块,对于同一区域的六个子块图像,将具有最大灰度平均值的那个子块所对应的特征图像方向作为该区域主方向,然后采用三位的二进制码对该主方向编码。掌纹区域方向码综合描述了纹线的灰度变化强度及其方向性信息,因而能更好地区分掌纹图像;此外,对区域的方向编码使该方法受图像平移和旋转影响也较小。在PolyU掌纹数据库上的实验结果验证了掌纹区域方向码方法的有效性。
(3)提出了一种基于散度差判别局部保持投影(Scatter-difference DiscriminantLocality Preserving Projections,SDLPP)的掌纹识别算法。子空间方法具有特征描述性强、计算代价小和良好可分性等优点,已被广泛应用于生物特征识别。线性鉴别分析(LDA)和局部保持投影(LPP)是其中比较经典的线性方法,LDA注重图像数据间的可分性,LPP则更关注于数据的局部关系。本文结合LDA的判别性思想和LPP注重局部邻域关系的思想,提出了散度差判别局部保持投影算法,并对SDLPP中影响图像分类的主要参数进行了研究分析。SDLPP的目的是使同类图像在低维空间中保持紧致的局部关系,同时使那些在图像空间中距离近的非同类图像在低维投影空间中相对远一些,从而使各类图像能获得更好地区分。在公开掌纹数据库上的实验结果表明提出的SDLPP算法较LDA、LPP具有更优的识别性能,对参数的分析则有助于确定SDLPP的最优参数。此外,论文还使用核方法在理论上将SDLPP扩展得到相应非线性算法,称为核散度差判别局部保持投影(Kernel SDLPP,KSDLPP)。
(4)设计了一种适合于智能移动设备的掌纹识别算法,并以该算法为核心开发了联想ET980智能手机实时掌纹身份验证系统。算法首先设计了一种适于移动设备的掌纹图像采集方式并给出了相应定位分割方法,这保证了移动设备下掌纹验证系统的可行性;在识别过程中加入光照预处理,一定程度上消除了外部光照变化的影响;采用Gabor小波变换和子空间方法的结合算法来提取掌纹特征,并用遗传算法对Gabor特征节点的数目和位置做优化选取,在保证识别精度同时显著地提高了识别效率。论文算法在联想ET980手机上可获得等错误率为3.42%的识别精度,实现的掌纹验证系统也满足识别系统的实时性要求。
Biometric technology provides a highly reliable and robust approach to personal verification. Palmprint recognition technology is a relatively new biometric technology but develops rapidly. Palmprint recognition technology is one of the most active and challenging research fields, and closely related to many disciplines such as Computer Vision, Pattern Recognition and Image Processing etc. Its research achievements would greatly contribute to the development of these disciplines. It is believed that automatic palmprint recognition would have a great deal of potential applications in information security, public security and law enforcement etc. Though palmprint recognition technology has made much progress, many problems still remain unsolved in practical applications. In this dissertation, the theory and algorithms of palmprint recognition are studied. The main contributions of the research work are as follows:
(1) A Log-Gabor wavelet phase congruency based palmprint line feature extraction approach is presented. Line feature is the most essential and remarkable feature of palmprint image, but its effective extraction is still a difficulty due to palm-lines' speciality and complexity. In this dissertation, a frequency domain algorithm called Log-Gabor wavelet phase congruency is adopt to extract palmprint line feature, including palmprint directional phase congruency feature (PDPCF) and palmprint global phase congruency feature(PGPCF). The proposed method can not only simultaneously extract structure, strength and width information of palm-lines, but provide well feature location accuracy. Palm-lines are then detected from PGPCF image and employed to recognition palmprint images. Experiment results show the effectiveness of the proposed line feature extraction approach.
(2) A palmprint region-based orientation code (PROC) scheme is proposed for palmprint line feature representation. In PROC, each PDPCF image is evenly divided into small sub-blocks. The orientation of one palmprint sub-region, which can be determined by comparison of the grey statistic results in six directions, is coded using three binary digits. PROC combines palm-lines' strength feature and orientation feature. It not only can provide better discriminant capability, but also is robust to the translation and rotation of palmprint image. Experiment results on PolyU palmprint database illuminate that the proposed PROC algorithm is effective for palmprint recognition.
(3) A scatter-difference discriminant locality preserving projections (SDLPP) algorithm is put forward for palmprint image recognition. Subspace methods have been widely applied for biometric recognition for their computational convenience, good feature representation and discrimination capability. Linear discriminant analysis (LDA) and locality preserving projections (LPP) are two classic linear methods. LDA focus on the separability of data while LPP pays more attention to samples' local relationship. SDLPP combines the basic ideas of LDA and LPP, aiming at preserving local relationship of image data from the same class while making the image classes nearby in image space be far from each other in projection space. The dissertation also presents a parameter selection method for SDLPP and extends SDLPP into its nonlinear form by the kernel theory. Experiment results illuminate that SDLPP outperforms LDA and LPP on recognition accuracy and the parameter selection method is effective.
(4) A mobile-based palmprint recognition algorithm is proposed, and then an embedded palmprint verification system running on Lenovo ET980 intelligent phone is developed. Firstly, the dissertation designs a novel palmprint image acquisition style and corresponding location and segmentation method, which ensures the feasibility of mobile-based palmprint recognition. Secondly, an illumination preprocessing operation is added to reduce the illumination variation influence. Finally, Gabor wavelet and subspace method are combined to extract palmprint feature. Then a genetic algorithm is employed to optimize the number and location of Gabor nodes, which can significantly improve the recognition efficiency. The proposed palmprint algorithm on Lenovo ET980 phone can achieve the recognition accuracy of EER=3.42%. The developed mobile-based palmprint verification system also satisfies the time requirement of a real-time verification system.
(1)提出了一种基于Log-Gabor小波相位一致的掌纹线特征提取方法。掌纹线特征是掌纹识别中最基本、最直观的特征,但由于掌纹线的特殊性和复杂性,如何有效提取掌纹线特征一直是掌纹识别的难点。本文从频率域角度考虑,使用Log-Gabor小波相位一致方法提取掌纹图像线特征,包括掌纹方向相位一致特征和掌纹整体相位一致特征。该方法提取的线特征可有效包含纹线的结构信息、强度信息和宽度信息,且提取的线特征比较稳定。在掌纹整体相位一致特征图像上进一步检测出具体的掌纹线,仅使用掌纹线结构特征来表示和识别掌纹,实验结果验证了Log-Gabor小波相位一致提取掌纹线特征用于识别的有效性。
(2)提出了基于区域方向码的掌纹线特征表示方法。该方法首先将6个方向的掌纹方向相位一致特征图像均匀分块,对于同一区域的六个子块图像,将具有最大灰度平均值的那个子块所对应的特征图像方向作为该区域主方向,然后采用三位的二进制码对该主方向编码。掌纹区域方向码综合描述了纹线的灰度变化强度及其方向性信息,因而能更好地区分掌纹图像;此外,对区域的方向编码使该方法受图像平移和旋转影响也较小。在PolyU掌纹数据库上的实验结果验证了掌纹区域方向码方法的有效性。
(3)提出了一种基于散度差判别局部保持投影(Scatter-difference DiscriminantLocality Preserving Projections,SDLPP)的掌纹识别算法。子空间方法具有特征描述性强、计算代价小和良好可分性等优点,已被广泛应用于生物特征识别。线性鉴别分析(LDA)和局部保持投影(LPP)是其中比较经典的线性方法,LDA注重图像数据间的可分性,LPP则更关注于数据的局部关系。本文结合LDA的判别性思想和LPP注重局部邻域关系的思想,提出了散度差判别局部保持投影算法,并对SDLPP中影响图像分类的主要参数进行了研究分析。SDLPP的目的是使同类图像在低维空间中保持紧致的局部关系,同时使那些在图像空间中距离近的非同类图像在低维投影空间中相对远一些,从而使各类图像能获得更好地区分。在公开掌纹数据库上的实验结果表明提出的SDLPP算法较LDA、LPP具有更优的识别性能,对参数的分析则有助于确定SDLPP的最优参数。此外,论文还使用核方法在理论上将SDLPP扩展得到相应非线性算法,称为核散度差判别局部保持投影(Kernel SDLPP,KSDLPP)。
(4)设计了一种适合于智能移动设备的掌纹识别算法,并以该算法为核心开发了联想ET980智能手机实时掌纹身份验证系统。算法首先设计了一种适于移动设备的掌纹图像采集方式并给出了相应定位分割方法,这保证了移动设备下掌纹验证系统的可行性;在识别过程中加入光照预处理,一定程度上消除了外部光照变化的影响;采用Gabor小波变换和子空间方法的结合算法来提取掌纹特征,并用遗传算法对Gabor特征节点的数目和位置做优化选取,在保证识别精度同时显著地提高了识别效率。论文算法在联想ET980手机上可获得等错误率为3.42%的识别精度,实现的掌纹验证系统也满足识别系统的实时性要求。
Biometric technology provides a highly reliable and robust approach to personal verification. Palmprint recognition technology is a relatively new biometric technology but develops rapidly. Palmprint recognition technology is one of the most active and challenging research fields, and closely related to many disciplines such as Computer Vision, Pattern Recognition and Image Processing etc. Its research achievements would greatly contribute to the development of these disciplines. It is believed that automatic palmprint recognition would have a great deal of potential applications in information security, public security and law enforcement etc. Though palmprint recognition technology has made much progress, many problems still remain unsolved in practical applications. In this dissertation, the theory and algorithms of palmprint recognition are studied. The main contributions of the research work are as follows:
(1) A Log-Gabor wavelet phase congruency based palmprint line feature extraction approach is presented. Line feature is the most essential and remarkable feature of palmprint image, but its effective extraction is still a difficulty due to palm-lines' speciality and complexity. In this dissertation, a frequency domain algorithm called Log-Gabor wavelet phase congruency is adopt to extract palmprint line feature, including palmprint directional phase congruency feature (PDPCF) and palmprint global phase congruency feature(PGPCF). The proposed method can not only simultaneously extract structure, strength and width information of palm-lines, but provide well feature location accuracy. Palm-lines are then detected from PGPCF image and employed to recognition palmprint images. Experiment results show the effectiveness of the proposed line feature extraction approach.
(2) A palmprint region-based orientation code (PROC) scheme is proposed for palmprint line feature representation. In PROC, each PDPCF image is evenly divided into small sub-blocks. The orientation of one palmprint sub-region, which can be determined by comparison of the grey statistic results in six directions, is coded using three binary digits. PROC combines palm-lines' strength feature and orientation feature. It not only can provide better discriminant capability, but also is robust to the translation and rotation of palmprint image. Experiment results on PolyU palmprint database illuminate that the proposed PROC algorithm is effective for palmprint recognition.
(3) A scatter-difference discriminant locality preserving projections (SDLPP) algorithm is put forward for palmprint image recognition. Subspace methods have been widely applied for biometric recognition for their computational convenience, good feature representation and discrimination capability. Linear discriminant analysis (LDA) and locality preserving projections (LPP) are two classic linear methods. LDA focus on the separability of data while LPP pays more attention to samples' local relationship. SDLPP combines the basic ideas of LDA and LPP, aiming at preserving local relationship of image data from the same class while making the image classes nearby in image space be far from each other in projection space. The dissertation also presents a parameter selection method for SDLPP and extends SDLPP into its nonlinear form by the kernel theory. Experiment results illuminate that SDLPP outperforms LDA and LPP on recognition accuracy and the parameter selection method is effective.
(4) A mobile-based palmprint recognition algorithm is proposed, and then an embedded palmprint verification system running on Lenovo ET980 intelligent phone is developed. Firstly, the dissertation designs a novel palmprint image acquisition style and corresponding location and segmentation method, which ensures the feasibility of mobile-based palmprint recognition. Secondly, an illumination preprocessing operation is added to reduce the illumination variation influence. Finally, Gabor wavelet and subspace method are combined to extract palmprint feature. Then a genetic algorithm is employed to optimize the number and location of Gabor nodes, which can significantly improve the recognition efficiency. The proposed palmprint algorithm on Lenovo ET980 phone can achieve the recognition accuracy of EER=3.42%. The developed mobile-based palmprint verification system also satisfies the time requirement of a real-time verification system.
引文
[1]RATHAN K,SENIOR A,BOILER M.Automated biometrics[C].Advances in Pattern Recognition -ICAPR 2001.Berlin:Springer.2001:445-453.
[2]PAGET F.Identity theft[R/OL].(2007,1,7)[2008,10].http://www,mcafee,com/us/threat center/white paper,html.
[3]LAWTON G.Biometric:A new Era in Security[J].Computer.1998,31(8):16-18.
[4]JAIN A K,ROSS A,PRABHAKAR S.An introduction to biometric recognition[J].IEEE Transaction on Circuit and System for Video Technology.2004,14(1):4-20.
[5]PANKANTI S,BOLLE R M,JAIN A K.Biometric:The future of identification[J].Computer.2000,33(2):46-49.
[6]田捷,杨鑫.生物特征识别技术理论与应用[M].北京,电子工业出版社,2005.
[7]中国自动识别网[OL].(2004,7,15)[2006,10].http://www.autoid-china.com.cn/html/2004-7-15/2004 715110056.asp.
[8]PRABHAKARS,PANKANTI S,JAIN A K.Biometric recognition:security & privacy concerns [J].IEEE Security & Privacy Magazine.2003,1(2):33-42.
[9]2008 International Biometric Group.Biometrics market and industry report 2009-2014[R/OL].(2008)[2008,10].http://www.biometricgroup.com/reports/public/market.report.html.
[10]2007生物特征识别技术及产品综合应用(中国)展览会.(2007,6)[2008,10].http://www.rfidworld.com.cn/expo/2007_6/20076211642590852.html.
[11]邬向前、张大鹏、王宽全.掌纹识别技术[M].科学出版社,北京,2006年10月.
[12]邬向前.面向身份识别的掌纹特征分析[D].哈尔滨:哈尔滨工业大学,2004.
[13]卢光明.掌纹识别系统关键技术与算法研究[D].哈尔滨:哈尔滨工业大学,2005.
[14]MATSUMOTO K.Palm-recognition systems:An ideal means of restricting access to high security areas[J].Mitsubishi Electric Advance.1985,131:31-32.
[15]SHIONO M,ISHIKAWA H,et al.An experiment on personal identification for gate security using hand shape and palm-print[J].Transactions of the Institute of Electronics,Info rmation and Communication Engineering D-II.1991,J74D-II(6):688-697.
[16]SHU W,ZHANG D.Palmprint verification:an implementation of biometric technology[C].IEEE International Conference on Pattern Recognition.1998,1:219-221.
[17]ZHANG D,SHU W.Two novel characteristics in palmprint verification:datum point invariance and line feature matching[J].Pattern Recognition.1999,33(4):691-702.
[18]SHU W,RONG G,BIAN Z.Automatic palmprint verification[J].International journal of Image and Graphics.2001,1(1):135-151.
[19]DUTAN,JAIN A K,MARDIA K V.Matching of palmprints[J].Pattern Recognition Letters.2002,23(4):477-485.
[20]HANC C,CflEN n L,LIN C L,et al.Personal authentication using palm-print features [J].Pattern Recognition.2003,36(2):371-381.
[21]LI W X,ZHANG D,YOU J,et al.A Texture-based approach to palmprint retrieval for personal identification[C].Proceeding of SPIE.CA:Society of Photo-Optical Instrumentation Engineers.2000:415-420.
[22]戴青云.形态金字塔与形态小波在掌纹分析中的应用研究[D].广州:华南理工大学,2001.
[23]魏鸿磊.自动指纹识别理论和算法研究[D].大连:大连理工大学,2007.
[24]黎明,严超华,刘高航.基于掌纹图象分析的身份识别系统[J].中国图象图形学报.2000,5(2):134-137.
[25]束为,荣钢,边肇祺等.利用掌纹进行身份自动鉴别方法的研究[J].清华大学学报(自然科学版).1999,39(1):95-97.
[26]束为,荣钢,边肇祺等.基于方向投影的自动掌纹基准点检测[J].清华大学学报(自然科学版).1999,39(1):98-100.
[27]LI W X,ZHANGD,XUZ Q.Palmprint recognition based on Fourier transform[J].Journal of Software.2002,13(5):879-886.
[28]ZHANG D,KONG W K,YOU J.Online palmprint identification[J].IEEE Trans.Pattern Analysis and Machine Intelligence.2003,25(9):1041-1050.
[29]KONG W K,ZHANG D,LI W X.Palmprint feature extraction using 2-D Gabor filters[J].Pattern Recognition.2003,36:2339-2347.
[30]The Hong Kong Polytechnic University.PolyU Palmprint Database.(2004,1,1)[2006,7,15].http://www.comp.polyu.edu.hk/~biometrics/.
[31]Zhang D.Palmprint Authentication[M].USA:Kluwer Academic Publisher.2004.
[32]陈鼎龙.中国手相学[M].乌鲁木齐.新疆大学出版社.1994.
[33]王晨霞.现代掌纹诊病[M].兰州.甘肃民族出版社.1995.
[34]YOU J,LI W X,ZHANG D.Hierarchical palmprint identification via multiple feature extraction[J].Pattern Recognition.2002,35(4):847-859.
[35]KUMAR A,WONGD,SHENH,et al.Personal verification using palmprint and hand geometry biometric[J].Lecture Notes in Computer Science.2003,2688:668-678.
[36]Biometric Authentication Competition 2005.(2005)[2005,10].http://www.cbsr.ia.ac.cn/conferences/.
[37]HAN C C.A hand-based personal authentication using a coarse-to-fine strategy[J].Pattern recognition.2003,36:371-381.
[38]CONNIE T,TEOHA,GOH M,et.al.Palmprint recognition with PCA and ICA[C].Preceedings of the Image and Vision Computing New Zealand.2003:227-232.
[39]ONG M G H,CONNIE T,JIN A T B,et al.A single-sensor hand geometry and palmprint verification system[C].Multimedia Biometrics Methods and Applications Workshop.Auguest 2003,lO0-106.
[40]ZHANG L AND ZHANG D.Characterization of palmprints by wavelet signatures via directional context modeling[J].IEEE Trans.Syst.Man Cybern.B.2004,34(3):1335-1347.
[41]LI W X,ZHANG D,XU Z Q.Image alignment based on invariant features for palmprint identification[J].Signal Process:Image Communication.May 2003,18(5):373-379.
[42]陈燕新,戚飞虎.一种新的提取轮廓特征点的方法[J].红外与毫米波学报.1998,17(3):171-176.
[43]戴青云,余英林,张大鹏.掌纹身份识别系统中的定位分割技术[J].广州工业大学学报.2002,19(1):1-5.
[44]WU X Q,WANG K Q,ZHANG D.Wavelet based palmprint recognition[C].Proceeding of the International Conference on Machine learning Cybernetics.2002:1253-1257.
[45]KUMAR A,WONG D C M,SHEN H C,et al.Personal authentication using hand images[J].Pattern Recognition Letters.2006,27:1478-1486.
[46]李文新,夏胜雄,张大鹏等.基于主线特征的双向匹配的掌纹识别新方法[J].计算机研究与发展.2004.41(6):996-1002.
[47]FUNADA J,OTHTA N,MIZOGUCHI M,et al.Feature extraction method for palmprint considering elimination of creases[C].Proceeding of the International conference on Pattern Recognition.USA:IEEE,1998:1849-1854.
[48]张泽,束为,荣刚.基于乳突纹方向特性的掌纹自动分类方法[J].清华大学学报(自然科学版).2002,42(9):1222-1224.
[49]YOU J,KONG W K,ZHANG D,et al.On hierarchical palmprint coding with multiple features for personal identification in large databases[J].IEEE Trans.Circuits Syst.Video Techn.2004,14(2):234-243.
[50]包桂秋,林喜荣,熊沈蜀等.掌纹图像处理方法的研究[J].计算机应用.2003,23(12):82-84.
[51]WU X Q,WANG K Q,ZHANG D.A novel approach of palmline extraction[C].Proceeding of 2006 International Conference on Image and Graphics.USA:IEEE,2004:230-233.
[52]WU X Q,ZHANG D,WANG K.Palm-line extraction and matching for personal authentiation [J].IEEE Transactions on Systems,Man,and Cybernetics,Part A.2006,36(5):978-987.
[53]LIU L,ZHANG D.A novel palm-line detector[C].Audio-and Video-Based Biometric Person Authentication.2005,36:563-571.
[54]MORAVEC H P.Towards automatic visual obstacle avoidance[C].Proceedings of the Fifth International Joint Conference on Articial Intelligence,Cambridge,MA.1977:584.
[55] KONG A W, ZHANG D. Feature-level fusion for effective palmprint authentication [C].Proceedings of the International Conference of Biometric Authentication, Lecture Notes in Computer Science. Germany: Springer. 2004:478-487.
[56] KONG A W, ZHANG D, KAMEL M. Palmprint identification using feature-level fusion [J].Pattern Recognition. 2006, 39(3):478-487.
[57] WU X Q, ZHANG D, WANG K Q. Fusion of phase and orientation information for palmprint authentication [J]. Pattern Analysis and Application. 2005, 9(2):103-111.
[58] 张俊.掌纹识别[D].大连:大连理工大学, 2006.
[59] LU G M, ZHANG D, WANG K Q. Palmprint recognition using eigenpalms features [J]. Pattern Recognition Letters. 2003, 24(9 - 10):1463-1467.
[60] WU X Q, ZHANG D, WANG K Q. Fisherpalms based palmprint recognition [J]. Pattern Recognition Letter. 2003, 24(15):2829-2838.
[61] CONNIE T, TEOH A, GOH M, et al. Palmprint recognition with PCA and ICA [C]. Proc. of Image and Vision Computing New Zealand, New Zealand (2003) 227-232.
[62] SHANG L, HUANG D S, DU J X, et al. Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network [J]. Neurocomputing. 2006: 1782-1786.
[63] ZUO W M. ZHANG D. WANG K Q. An assembled matrix distance metric for 2DPCA-based image recognition [J]. Pattern Recognition Letter.2006, 27(3):210-216.
[64] HU D W, FENG G Y, ZHOU Z T. Two-dimensional locality preserving projections with its application to palmprint recognition [J]. Pattern Recognition. 2007, 40(1):339-342.
[65] SU X S, LIN X R, DING T H, et al. Palmprint feature extraction based wavelet transform [J]. Journal of Tsinghua University (Science &Technology). 2003, 43(8):1049-1051.
[66] ZHANG L, ZHANG D. Characterization of Palmprints by wavelets signatures via directional context modeling [J]. IEEE Trans. Syst. Man Cybern. 2004, 34(3):1335-1347.
[67] WUXQ, WANG K Q, ZHANG D. Wavelet based palmprint recognition [C]. Conference on Machine Learning and Cybernetics, Beijing. 2002, 3(7):1253-1257.
[68] WU X Q, WANG K Q, ZHANG D. Wavelet energy feature extraction and matching for palmprint recognition [J]. Journal of Computer Science and Technology. 2005, 20(3) :411-418.
[69] ITO K, NAKAJIMA H, KOBAYASHI K, et al. A fingerprint matching algorithm using phase-only correlation [J].IEICE Trans. Fundamentals. 2004, E87-A(3):682 - 691.
[70] Miyazawa K, Ito K, Aoki T, Kobayashi K and Nakajima H. A phase-based iris recognition algorithm [J].Lecture Notes in Computer Science. 2005, 3832: 356-365.
[71] ITO K, AOKI T, NAKAJIMA H, et al. A phase-based palmprint recognition algorithm and its experimental evaluation [J]. Intelligent Signal Processing and Communication. 2006:215-218.
[72] ITO K, AOKI T, NAKAJIMA H, ET AL. A palmprint recognition algorithm using phase-only correlation [J].IEICE Transactions. 2008:1023-1030.
[73]HENNINGS P,KUMAR B V K V.Palmprint recognition using correlation filter classifiers [C].Conference Record 38th Asilomar Conference on Signals.2004,(1):567-571.
[74]吴介,裘正定,掌纹识别中的特征提取算法综述[J].北京电子科技学院学报.2005,2(13),86-92.
[75]PANG YI H,CONNIE T,JIN A T B,et al.Palmprint authentication with Zernike moment invariants[C].Signal Processing and Information Technology--Proceedings of the 3rd IEEE International Symposium.2003:199-202.
[76]PANG Y ANDREW T B J,DAVID N C L,et al.Palmprint verification with moments[J].Journal of WSCG.2003,(12):1-3.
[77]WUX Q,ZHANG F M,WANG K Q,et al.Fusion of the textural feature and palm-lines for palmprint authentication[C].Lecture Notes in Computer Science,ICIC05,2005,(3644):1075-1084.
[78]KUMAR A,ZHANG D.Personal authentication using multiple palmprint representation[J].Pattern Recognition.2005,38(10):1695-1704.
[79]YOU J,KONG W K,ZHANG D,et al.On hierarchical palmprint coding with multiple features for personal identification in large databases[J].IEEE Trans.Circuits Syst.Video Techn.2004,14(2):234-243.
[80]邬向前,王宽全,张大鹏.一种用于掌纹识别的线特征表示和匹配方法(英文)[J].2004,15(6):869-880.
[81]WU X Q,WANG KQ,ZHANG D.Fuzzy directional element energy feature(FDEEF) based palmprint identification[C].The 16th IEEE International Conference on Pattern Recognition.2002,1:95-98.
[82]BUBECK U M.Multibiometric authentication-an overview of recent developments[R].San Diego University.2003.
[83]ROSS A,JAIN A K.Information fusion in biometrics[J].Pattern Recognition Letters.2003,24(13):2115-2125.
[84]HONG L,JAIN A K,PANKANTI S.Can multibiometrics improve performance?[C].In:Proc.AutoID'99,Summit,NJ,USA.1999:59-64.
[85]JAIN A K,ROSS A,PANKANTI S.A prototype hand geometry-based verification system[C].Second International Conference on Audio and Video-based Biometric Person Authentication(AVBPA),Washington,D.C.,USA.1999,166-171.
[86]SHOBHA+ G,KRISHNA M,SHARMA S C.Development of palmprint verification system using biometrics[J].软件学报.2006.17(8):1824-1836.
[87]RIBARIC S,FRATIC L.A Biometric identification system based on Eigenpalm and Eigenfiger Features[J].IEEE Trans.on PAMI.2005,27(11):1698-1709
[88]李强.手部特征识别及特征级融合算法研究[D].北京:北京交通大学,2006.
[89]王国强.嵌入邻域判别关系的子空间人脸识别算法研究[D].大连:大连理工大学,2008.
[90] KOVESI P. Image features from phase congruency [J].Vido Journal of Computer Vision Research. 1991, (3): 1-27.
[91] MORRONE M C, ROSS J R, OWENS R A, et al. Mach bands are phase dependent [J]. Nature.1986, 324(6094): 250-253.
[92] KOVESI P. Phase congruency detects corners and edges [C]. The Australian Pattern Recognition Society Conference: DICTA 2003. Sydney. 2003:309-318.
[93] MORRONE M C, OWENS R A. Feature detection from local energy [J].Pattern Recognition Letters. 1987, 6:303-313.
[94] MORRONE M C, BURR D C. Feature detection in human vision: a phase-dependent energy model [C]. In: Proceedings of the Royal Society, London B. 1988, 235: 221-245.
[95] KOVESI P. Invariant measures of image features from phase information [D].The University of Western Australia. 1996. 5.
[96] VENKATESH S, OWENS R. An energy feature detection scheme [C]. ICCV. Singapore.1989:553-557.
[97] LIN C L, CHUANG T C, FAN K C. Palmprint verification using hierarchical decomposition [J]. Pattern Recognition. 2005, 38(12), 2639-2652.
[98] KONG W K, ZHANG D. Competitive coding scheme for palmprint verification [C]. Proceedings of International Conference on Pattern Recognition. 2004,1:520-523.
[99] UST Hand Image Database [Online]. Available: http://visgraph.cs.ust.hk/biometrics/-Visgraph_web/index. html.
[100] TURK M, PENTLAND A. Eigenface for recognition [J]. Journal of Cognitive Neuoscience,1991, (3): 71-86.
[101] PENTLAND A, MOGHADDAM B, STARNER T. View-based and modular eigenspaces for face recognition [C].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle WA: IEEE Computer Society Press. 1994:84-91.
[102] BELHUMEUR P, HESPANHA J, KRIEGMAN D. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997, 19(7):711-720.
[103] MIN W, LU K, HE X F. Locality preserving projection [J].Pattern Recognition. 2004,37(4): 781-788.
[104] HE X F, NIYOGI P. Locality preserving projections [C]. Proceedings of Advances in Neural Information Processing Systems 16, Vancouver, Canada. 2003: 153-160.
[105] 边肇祺.模式识别[M].北京:清华大学出版社, 1988.
[106] TENENBAUM J B, DE SILVA V, LANGFORD J C. A Global geometric framework for nonlinear dimensionality reduction [J]. Science. 2000, 290(5500):2319-2323.
[107] ROWEIS S L, SAUL L. Nonlinear dimensionality reduction by locally linear embedding [J].Science, 2000, 290(5500):2323-2326.
[108] SAUL L, ROWEIS S. Think globally, fit locally: unsupervised learning of nonlinear manifolds [J]. Journal of Machine Learning Research. 2003, 4:119-155.
[109] YANG M H. Face recognition using extended isomap [C]. Proceeding of ICIP. September,2002, 2:117-120.
[110] SEUNG H S, LEE D D. The manifold ways of perceptions [J]. Science. 2000, 290(12):2268-2269.
[111] SHASHUA A, LEVIN A, AVIDAN S. Manifold pursuit: a new approach to appearance based recognition [C]. Proc.Int'l Conf. Pattern Recognition. 2002:590-594.
[112] BELKIN M, NIYOGI P. Laplacian Eigenmaps for dimensionality reduction and data representation [J]. Neural Computation. 2003, 15(6):1373-1396.
[113] ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment [J].SIAM Journal of Scientific Computing. 2004,26(1):313-338.
[114] ZHANG C S, WANG J, ZHAO N Y, et al. Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction [J]. Pattern Recognition. 2004,37:325-336.
[115] FISHER R A. The use of multiple measurements in taxonomic problems [J]. Ann Eugenics.1936, 7:178-188.
[116] WANG X G, TANG X O. Unified subspace analysis for dace recognition [C].Proc. Int' 1 Conf. Computer Vision. 2003:679-686.
[117] WANG X G, TANG X 0. A unified framework for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004, 26(9): 1222-1228.
[118] WEI Q L, WANG R S, XU B, et al. Mathematical programming and optimization design [M].Design. Beijing: National Defense Industry Press, 1984.
[119] VAPNIK V N. The nature of statistical learning theory [M].New York: Springer Verlag,1995.
[120] 边肇祺,张学工等.模式识别[M].清华大学出版社. 2000.
[121] MULLER K R, MIKA S, RATSCH G, et al. An introduction to kernel-based learning algorithms [J].IEEE Transactions on Neural Networks. 2001, 12(2):181-201.
[122] SCHOLKOPF B, SMOLA A, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem [J].Neural Computation. 1998,10(5):1922-1319.
[123] YANG M H. Kernel Eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods [C]. Proceedings of International Comference on Automatic Face and Gesture Recognition, USA, Washington DC. 2002:215-220.
[124] University of Cambridge. ORL face database[0L]. (2002)[2004, 6].http://www. cl. cam. ac. uk/research/dtg/attarchive/face-database, html.
[125] Federal Communications Commission. Cell Phone Fraud [OL]. (08, 6, 11)[08,12,12]. http://www.fcc.gov/cgb/consumerfacts/cellphonefraud.html.
[126]JANSEN W,SCARFONE K.Guidelines on cell phone and PDA security[M/OL].USA:National Institute of Standards and Technology.2008.(2008,12)[2008,12].http://csrc,nist.gov/publications/-drafts/800-124/Draft-SP800-124,pdf.
[127]JOHSON D J,RAHMANZ,WOODELL G A.Properties and performance of a center/surround retinex.IEEE Transaction on Image Processing.1997,(6):451-462.
[128]GABOR D.Theory of communication[J].Inst.of Electrical Engineering.1946,93(3):429-457.
[129]唐旭晟.基于机器学习和互信息分析的人脸检测和识别[D].大连:大连理工大学,2007.
[130]李云峰.基于Gabor小波变换的人脸识别[D].大连:大连理工大学,2006.
[131]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[132]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社.2002.
[133]HAN Y F,TAN T N,SUN Z N,et al.Embedded palmprint recognition system on mobile devices[C].ICB 2007:1184-1193.
[134]LEE T S.Image representation using 2D Gabor wavelets[J].IEEE Transactions on PAMI.1996,18(10):959-971.
[135]DAUGMAN J G.Two-dimensional spectral analysis of cortical receptive field profile [J].Vision Research.1980,20:847-856.
[136]DAUGMAN J G.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters[J].Journal of the Optical Society of America A.1985,2(7):1160-1169.
[2]PAGET F.Identity theft[R/OL].(2007,1,7)[2008,10].http://www,mcafee,com/us/threat center/white paper,html.
[3]LAWTON G.Biometric:A new Era in Security[J].Computer.1998,31(8):16-18.
[4]JAIN A K,ROSS A,PRABHAKAR S.An introduction to biometric recognition[J].IEEE Transaction on Circuit and System for Video Technology.2004,14(1):4-20.
[5]PANKANTI S,BOLLE R M,JAIN A K.Biometric:The future of identification[J].Computer.2000,33(2):46-49.
[6]田捷,杨鑫.生物特征识别技术理论与应用[M].北京,电子工业出版社,2005.
[7]中国自动识别网[OL].(2004,7,15)[2006,10].http://www.autoid-china.com.cn/html/2004-7-15/2004 715110056.asp.
[8]PRABHAKARS,PANKANTI S,JAIN A K.Biometric recognition:security & privacy concerns [J].IEEE Security & Privacy Magazine.2003,1(2):33-42.
[9]2008 International Biometric Group.Biometrics market and industry report 2009-2014[R/OL].(2008)[2008,10].http://www.biometricgroup.com/reports/public/market.report.html.
[10]2007生物特征识别技术及产品综合应用(中国)展览会.(2007,6)[2008,10].http://www.rfidworld.com.cn/expo/2007_6/20076211642590852.html.
[11]邬向前、张大鹏、王宽全.掌纹识别技术[M].科学出版社,北京,2006年10月.
[12]邬向前.面向身份识别的掌纹特征分析[D].哈尔滨:哈尔滨工业大学,2004.
[13]卢光明.掌纹识别系统关键技术与算法研究[D].哈尔滨:哈尔滨工业大学,2005.
[14]MATSUMOTO K.Palm-recognition systems:An ideal means of restricting access to high security areas[J].Mitsubishi Electric Advance.1985,131:31-32.
[15]SHIONO M,ISHIKAWA H,et al.An experiment on personal identification for gate security using hand shape and palm-print[J].Transactions of the Institute of Electronics,Info rmation and Communication Engineering D-II.1991,J74D-II(6):688-697.
[16]SHU W,ZHANG D.Palmprint verification:an implementation of biometric technology[C].IEEE International Conference on Pattern Recognition.1998,1:219-221.
[17]ZHANG D,SHU W.Two novel characteristics in palmprint verification:datum point invariance and line feature matching[J].Pattern Recognition.1999,33(4):691-702.
[18]SHU W,RONG G,BIAN Z.Automatic palmprint verification[J].International journal of Image and Graphics.2001,1(1):135-151.
[19]DUTAN,JAIN A K,MARDIA K V.Matching of palmprints[J].Pattern Recognition Letters.2002,23(4):477-485.
[20]HANC C,CflEN n L,LIN C L,et al.Personal authentication using palm-print features [J].Pattern Recognition.2003,36(2):371-381.
[21]LI W X,ZHANG D,YOU J,et al.A Texture-based approach to palmprint retrieval for personal identification[C].Proceeding of SPIE.CA:Society of Photo-Optical Instrumentation Engineers.2000:415-420.
[22]戴青云.形态金字塔与形态小波在掌纹分析中的应用研究[D].广州:华南理工大学,2001.
[23]魏鸿磊.自动指纹识别理论和算法研究[D].大连:大连理工大学,2007.
[24]黎明,严超华,刘高航.基于掌纹图象分析的身份识别系统[J].中国图象图形学报.2000,5(2):134-137.
[25]束为,荣钢,边肇祺等.利用掌纹进行身份自动鉴别方法的研究[J].清华大学学报(自然科学版).1999,39(1):95-97.
[26]束为,荣钢,边肇祺等.基于方向投影的自动掌纹基准点检测[J].清华大学学报(自然科学版).1999,39(1):98-100.
[27]LI W X,ZHANGD,XUZ Q.Palmprint recognition based on Fourier transform[J].Journal of Software.2002,13(5):879-886.
[28]ZHANG D,KONG W K,YOU J.Online palmprint identification[J].IEEE Trans.Pattern Analysis and Machine Intelligence.2003,25(9):1041-1050.
[29]KONG W K,ZHANG D,LI W X.Palmprint feature extraction using 2-D Gabor filters[J].Pattern Recognition.2003,36:2339-2347.
[30]The Hong Kong Polytechnic University.PolyU Palmprint Database.(2004,1,1)[2006,7,15].http://www.comp.polyu.edu.hk/~biometrics/.
[31]Zhang D.Palmprint Authentication[M].USA:Kluwer Academic Publisher.2004.
[32]陈鼎龙.中国手相学[M].乌鲁木齐.新疆大学出版社.1994.
[33]王晨霞.现代掌纹诊病[M].兰州.甘肃民族出版社.1995.
[34]YOU J,LI W X,ZHANG D.Hierarchical palmprint identification via multiple feature extraction[J].Pattern Recognition.2002,35(4):847-859.
[35]KUMAR A,WONGD,SHENH,et al.Personal verification using palmprint and hand geometry biometric[J].Lecture Notes in Computer Science.2003,2688:668-678.
[36]Biometric Authentication Competition 2005.(2005)[2005,10].http://www.cbsr.ia.ac.cn/conferences/.
[37]HAN C C.A hand-based personal authentication using a coarse-to-fine strategy[J].Pattern recognition.2003,36:371-381.
[38]CONNIE T,TEOHA,GOH M,et.al.Palmprint recognition with PCA and ICA[C].Preceedings of the Image and Vision Computing New Zealand.2003:227-232.
[39]ONG M G H,CONNIE T,JIN A T B,et al.A single-sensor hand geometry and palmprint verification system[C].Multimedia Biometrics Methods and Applications Workshop.Auguest 2003,lO0-106.
[40]ZHANG L AND ZHANG D.Characterization of palmprints by wavelet signatures via directional context modeling[J].IEEE Trans.Syst.Man Cybern.B.2004,34(3):1335-1347.
[41]LI W X,ZHANG D,XU Z Q.Image alignment based on invariant features for palmprint identification[J].Signal Process:Image Communication.May 2003,18(5):373-379.
[42]陈燕新,戚飞虎.一种新的提取轮廓特征点的方法[J].红外与毫米波学报.1998,17(3):171-176.
[43]戴青云,余英林,张大鹏.掌纹身份识别系统中的定位分割技术[J].广州工业大学学报.2002,19(1):1-5.
[44]WU X Q,WANG K Q,ZHANG D.Wavelet based palmprint recognition[C].Proceeding of the International Conference on Machine learning Cybernetics.2002:1253-1257.
[45]KUMAR A,WONG D C M,SHEN H C,et al.Personal authentication using hand images[J].Pattern Recognition Letters.2006,27:1478-1486.
[46]李文新,夏胜雄,张大鹏等.基于主线特征的双向匹配的掌纹识别新方法[J].计算机研究与发展.2004.41(6):996-1002.
[47]FUNADA J,OTHTA N,MIZOGUCHI M,et al.Feature extraction method for palmprint considering elimination of creases[C].Proceeding of the International conference on Pattern Recognition.USA:IEEE,1998:1849-1854.
[48]张泽,束为,荣刚.基于乳突纹方向特性的掌纹自动分类方法[J].清华大学学报(自然科学版).2002,42(9):1222-1224.
[49]YOU J,KONG W K,ZHANG D,et al.On hierarchical palmprint coding with multiple features for personal identification in large databases[J].IEEE Trans.Circuits Syst.Video Techn.2004,14(2):234-243.
[50]包桂秋,林喜荣,熊沈蜀等.掌纹图像处理方法的研究[J].计算机应用.2003,23(12):82-84.
[51]WU X Q,WANG K Q,ZHANG D.A novel approach of palmline extraction[C].Proceeding of 2006 International Conference on Image and Graphics.USA:IEEE,2004:230-233.
[52]WU X Q,ZHANG D,WANG K.Palm-line extraction and matching for personal authentiation [J].IEEE Transactions on Systems,Man,and Cybernetics,Part A.2006,36(5):978-987.
[53]LIU L,ZHANG D.A novel palm-line detector[C].Audio-and Video-Based Biometric Person Authentication.2005,36:563-571.
[54]MORAVEC H P.Towards automatic visual obstacle avoidance[C].Proceedings of the Fifth International Joint Conference on Articial Intelligence,Cambridge,MA.1977:584.
[55] KONG A W, ZHANG D. Feature-level fusion for effective palmprint authentication [C].Proceedings of the International Conference of Biometric Authentication, Lecture Notes in Computer Science. Germany: Springer. 2004:478-487.
[56] KONG A W, ZHANG D, KAMEL M. Palmprint identification using feature-level fusion [J].Pattern Recognition. 2006, 39(3):478-487.
[57] WU X Q, ZHANG D, WANG K Q. Fusion of phase and orientation information for palmprint authentication [J]. Pattern Analysis and Application. 2005, 9(2):103-111.
[58] 张俊.掌纹识别[D].大连:大连理工大学, 2006.
[59] LU G M, ZHANG D, WANG K Q. Palmprint recognition using eigenpalms features [J]. Pattern Recognition Letters. 2003, 24(9 - 10):1463-1467.
[60] WU X Q, ZHANG D, WANG K Q. Fisherpalms based palmprint recognition [J]. Pattern Recognition Letter. 2003, 24(15):2829-2838.
[61] CONNIE T, TEOH A, GOH M, et al. Palmprint recognition with PCA and ICA [C]. Proc. of Image and Vision Computing New Zealand, New Zealand (2003) 227-232.
[62] SHANG L, HUANG D S, DU J X, et al. Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network [J]. Neurocomputing. 2006: 1782-1786.
[63] ZUO W M. ZHANG D. WANG K Q. An assembled matrix distance metric for 2DPCA-based image recognition [J]. Pattern Recognition Letter.2006, 27(3):210-216.
[64] HU D W, FENG G Y, ZHOU Z T. Two-dimensional locality preserving projections with its application to palmprint recognition [J]. Pattern Recognition. 2007, 40(1):339-342.
[65] SU X S, LIN X R, DING T H, et al. Palmprint feature extraction based wavelet transform [J]. Journal of Tsinghua University (Science &Technology). 2003, 43(8):1049-1051.
[66] ZHANG L, ZHANG D. Characterization of Palmprints by wavelets signatures via directional context modeling [J]. IEEE Trans. Syst. Man Cybern. 2004, 34(3):1335-1347.
[67] WUXQ, WANG K Q, ZHANG D. Wavelet based palmprint recognition [C]. Conference on Machine Learning and Cybernetics, Beijing. 2002, 3(7):1253-1257.
[68] WU X Q, WANG K Q, ZHANG D. Wavelet energy feature extraction and matching for palmprint recognition [J]. Journal of Computer Science and Technology. 2005, 20(3) :411-418.
[69] ITO K, NAKAJIMA H, KOBAYASHI K, et al. A fingerprint matching algorithm using phase-only correlation [J].IEICE Trans. Fundamentals. 2004, E87-A(3):682 - 691.
[70] Miyazawa K, Ito K, Aoki T, Kobayashi K and Nakajima H. A phase-based iris recognition algorithm [J].Lecture Notes in Computer Science. 2005, 3832: 356-365.
[71] ITO K, AOKI T, NAKAJIMA H, et al. A phase-based palmprint recognition algorithm and its experimental evaluation [J]. Intelligent Signal Processing and Communication. 2006:215-218.
[72] ITO K, AOKI T, NAKAJIMA H, ET AL. A palmprint recognition algorithm using phase-only correlation [J].IEICE Transactions. 2008:1023-1030.
[73]HENNINGS P,KUMAR B V K V.Palmprint recognition using correlation filter classifiers [C].Conference Record 38th Asilomar Conference on Signals.2004,(1):567-571.
[74]吴介,裘正定,掌纹识别中的特征提取算法综述[J].北京电子科技学院学报.2005,2(13),86-92.
[75]PANG YI H,CONNIE T,JIN A T B,et al.Palmprint authentication with Zernike moment invariants[C].Signal Processing and Information Technology--Proceedings of the 3rd IEEE International Symposium.2003:199-202.
[76]PANG Y ANDREW T B J,DAVID N C L,et al.Palmprint verification with moments[J].Journal of WSCG.2003,(12):1-3.
[77]WUX Q,ZHANG F M,WANG K Q,et al.Fusion of the textural feature and palm-lines for palmprint authentication[C].Lecture Notes in Computer Science,ICIC05,2005,(3644):1075-1084.
[78]KUMAR A,ZHANG D.Personal authentication using multiple palmprint representation[J].Pattern Recognition.2005,38(10):1695-1704.
[79]YOU J,KONG W K,ZHANG D,et al.On hierarchical palmprint coding with multiple features for personal identification in large databases[J].IEEE Trans.Circuits Syst.Video Techn.2004,14(2):234-243.
[80]邬向前,王宽全,张大鹏.一种用于掌纹识别的线特征表示和匹配方法(英文)[J].2004,15(6):869-880.
[81]WU X Q,WANG KQ,ZHANG D.Fuzzy directional element energy feature(FDEEF) based palmprint identification[C].The 16th IEEE International Conference on Pattern Recognition.2002,1:95-98.
[82]BUBECK U M.Multibiometric authentication-an overview of recent developments[R].San Diego University.2003.
[83]ROSS A,JAIN A K.Information fusion in biometrics[J].Pattern Recognition Letters.2003,24(13):2115-2125.
[84]HONG L,JAIN A K,PANKANTI S.Can multibiometrics improve performance?[C].In:Proc.AutoID'99,Summit,NJ,USA.1999:59-64.
[85]JAIN A K,ROSS A,PANKANTI S.A prototype hand geometry-based verification system[C].Second International Conference on Audio and Video-based Biometric Person Authentication(AVBPA),Washington,D.C.,USA.1999,166-171.
[86]SHOBHA+ G,KRISHNA M,SHARMA S C.Development of palmprint verification system using biometrics[J].软件学报.2006.17(8):1824-1836.
[87]RIBARIC S,FRATIC L.A Biometric identification system based on Eigenpalm and Eigenfiger Features[J].IEEE Trans.on PAMI.2005,27(11):1698-1709
[88]李强.手部特征识别及特征级融合算法研究[D].北京:北京交通大学,2006.
[89]王国强.嵌入邻域判别关系的子空间人脸识别算法研究[D].大连:大连理工大学,2008.
[90] KOVESI P. Image features from phase congruency [J].Vido Journal of Computer Vision Research. 1991, (3): 1-27.
[91] MORRONE M C, ROSS J R, OWENS R A, et al. Mach bands are phase dependent [J]. Nature.1986, 324(6094): 250-253.
[92] KOVESI P. Phase congruency detects corners and edges [C]. The Australian Pattern Recognition Society Conference: DICTA 2003. Sydney. 2003:309-318.
[93] MORRONE M C, OWENS R A. Feature detection from local energy [J].Pattern Recognition Letters. 1987, 6:303-313.
[94] MORRONE M C, BURR D C. Feature detection in human vision: a phase-dependent energy model [C]. In: Proceedings of the Royal Society, London B. 1988, 235: 221-245.
[95] KOVESI P. Invariant measures of image features from phase information [D].The University of Western Australia. 1996. 5.
[96] VENKATESH S, OWENS R. An energy feature detection scheme [C]. ICCV. Singapore.1989:553-557.
[97] LIN C L, CHUANG T C, FAN K C. Palmprint verification using hierarchical decomposition [J]. Pattern Recognition. 2005, 38(12), 2639-2652.
[98] KONG W K, ZHANG D. Competitive coding scheme for palmprint verification [C]. Proceedings of International Conference on Pattern Recognition. 2004,1:520-523.
[99] UST Hand Image Database [Online]. Available: http://visgraph.cs.ust.hk/biometrics/-Visgraph_web/index. html.
[100] TURK M, PENTLAND A. Eigenface for recognition [J]. Journal of Cognitive Neuoscience,1991, (3): 71-86.
[101] PENTLAND A, MOGHADDAM B, STARNER T. View-based and modular eigenspaces for face recognition [C].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle WA: IEEE Computer Society Press. 1994:84-91.
[102] BELHUMEUR P, HESPANHA J, KRIEGMAN D. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997, 19(7):711-720.
[103] MIN W, LU K, HE X F. Locality preserving projection [J].Pattern Recognition. 2004,37(4): 781-788.
[104] HE X F, NIYOGI P. Locality preserving projections [C]. Proceedings of Advances in Neural Information Processing Systems 16, Vancouver, Canada. 2003: 153-160.
[105] 边肇祺.模式识别[M].北京:清华大学出版社, 1988.
[106] TENENBAUM J B, DE SILVA V, LANGFORD J C. A Global geometric framework for nonlinear dimensionality reduction [J]. Science. 2000, 290(5500):2319-2323.
[107] ROWEIS S L, SAUL L. Nonlinear dimensionality reduction by locally linear embedding [J].Science, 2000, 290(5500):2323-2326.
[108] SAUL L, ROWEIS S. Think globally, fit locally: unsupervised learning of nonlinear manifolds [J]. Journal of Machine Learning Research. 2003, 4:119-155.
[109] YANG M H. Face recognition using extended isomap [C]. Proceeding of ICIP. September,2002, 2:117-120.
[110] SEUNG H S, LEE D D. The manifold ways of perceptions [J]. Science. 2000, 290(12):2268-2269.
[111] SHASHUA A, LEVIN A, AVIDAN S. Manifold pursuit: a new approach to appearance based recognition [C]. Proc.Int'l Conf. Pattern Recognition. 2002:590-594.
[112] BELKIN M, NIYOGI P. Laplacian Eigenmaps for dimensionality reduction and data representation [J]. Neural Computation. 2003, 15(6):1373-1396.
[113] ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment [J].SIAM Journal of Scientific Computing. 2004,26(1):313-338.
[114] ZHANG C S, WANG J, ZHAO N Y, et al. Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction [J]. Pattern Recognition. 2004,37:325-336.
[115] FISHER R A. The use of multiple measurements in taxonomic problems [J]. Ann Eugenics.1936, 7:178-188.
[116] WANG X G, TANG X O. Unified subspace analysis for dace recognition [C].Proc. Int' 1 Conf. Computer Vision. 2003:679-686.
[117] WANG X G, TANG X 0. A unified framework for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2004, 26(9): 1222-1228.
[118] WEI Q L, WANG R S, XU B, et al. Mathematical programming and optimization design [M].Design. Beijing: National Defense Industry Press, 1984.
[119] VAPNIK V N. The nature of statistical learning theory [M].New York: Springer Verlag,1995.
[120] 边肇祺,张学工等.模式识别[M].清华大学出版社. 2000.
[121] MULLER K R, MIKA S, RATSCH G, et al. An introduction to kernel-based learning algorithms [J].IEEE Transactions on Neural Networks. 2001, 12(2):181-201.
[122] SCHOLKOPF B, SMOLA A, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem [J].Neural Computation. 1998,10(5):1922-1319.
[123] YANG M H. Kernel Eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods [C]. Proceedings of International Comference on Automatic Face and Gesture Recognition, USA, Washington DC. 2002:215-220.
[124] University of Cambridge. ORL face database[0L]. (2002)[2004, 6].http://www. cl. cam. ac. uk/research/dtg/attarchive/face-database, html.
[125] Federal Communications Commission. Cell Phone Fraud [OL]. (08, 6, 11)[08,12,12]. http://www.fcc.gov/cgb/consumerfacts/cellphonefraud.html.
[126]JANSEN W,SCARFONE K.Guidelines on cell phone and PDA security[M/OL].USA:National Institute of Standards and Technology.2008.(2008,12)[2008,12].http://csrc,nist.gov/publications/-drafts/800-124/Draft-SP800-124,pdf.
[127]JOHSON D J,RAHMANZ,WOODELL G A.Properties and performance of a center/surround retinex.IEEE Transaction on Image Processing.1997,(6):451-462.
[128]GABOR D.Theory of communication[J].Inst.of Electrical Engineering.1946,93(3):429-457.
[129]唐旭晟.基于机器学习和互信息分析的人脸检测和识别[D].大连:大连理工大学,2007.
[130]李云峰.基于Gabor小波变换的人脸识别[D].大连:大连理工大学,2006.
[131]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[132]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社.2002.
[133]HAN Y F,TAN T N,SUN Z N,et al.Embedded palmprint recognition system on mobile devices[C].ICB 2007:1184-1193.
[134]LEE T S.Image representation using 2D Gabor wavelets[J].IEEE Transactions on PAMI.1996,18(10):959-971.
[135]DAUGMAN J G.Two-dimensional spectral analysis of cortical receptive field profile [J].Vision Research.1980,20:847-856.
[136]DAUGMAN J G.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters[J].Journal of the Optical Society of America A.1985,2(7):1160-1169.