摘要
多模态生物特征识别,作为一种新型的、凭借用户自身固有的属性特征进行身份识别,近年来日趋成为国际上的研究热点。其应用涉及商业、安全和司法等领域,可用于自动视频监控、访问控制、身份鉴定、计算机人/机界面设计、银行ATM机等。
作为生物特征识别的主要方法之一,人工神经网络自80年代初复苏以来一直是科学与工程上的一个热点研究学科,吸引了包括MIT、Harvard大学等国际一流学术机构的科学家从事此领域研究,并取得了大量的研究成果,《Science》、《Nature》等许多国际一流学术刊物常有相关研究成果发表。
本论文重点研究多模态生物特征融合的神经网络方法,主要研究了如何利用神经网络方法提取人脸的局部特征和全局特征,并进行身份识别。其中主要研究了PCA主元分析、带稀疏度约束的非负矩阵分解NMFs、径向基神经网络RBF、Fisher线性判别法等方法,并用于含噪声的人脸图像识别(本文不涉及人脸检测)。研究了基于固定吸引点的联想神经网络,构建了能学习连续吸引子的子空间联想存储器,用于局部残缺、局部遮挡的人脸图像识别。研究了连续粒子群优化算法PSO模型及二进制离散PSO模型,并利用人工免疫阴性选择机制控制粒子的最低及最高飞行速度。研究了自适应模糊神经推理、自适应Bayes决策融合策略、以及自适应Bayes优化的人工免疫二进制PSO模型(用于寻找最优融合规则),并利用自适应模糊神经推理和自适应Bayes优化的人工免疫二进制PSO算法解决人脸全局特征与局部特征融合、人脸与指纹融合等多模态生物特征融合的身份识别问题。
本论文的主要研究成果和创新如下:
1.提出并改进了基于神经网络的人脸图像的局部特征提取与识别方法,将带稀疏度约束的非负矩阵分解NMFs与Fisher线性判别法结合起来,利用径向基神经网络RBF作为分类器,对识别不同光照、采自不同头部姿势、不同表情的、含局部遮挡噪声的人脸图像,具有显著的优势,系统性能明显提高;
2.建立了具有连续吸引子的子空间联想神经网络模型,与具有固定吸引点的联想神经网络相比,子空间联想神经网络能恢复填充输入人脸图像的残缺或遮挡部分,并能显著提高部分残缺/遮挡的人脸图像的识别性能;
3.提出了最低/最高速度约束的连续粒子群优化算法,并用于训练多层感知机,能有效保证在搜索函数的最优解时,算法的收敛速度更快,收敛能力更强,并且能避开局部最优,收敛到全局最优;
4.提出了人脸全局特征与局部特征融合的自适应模糊神经推理,以及自适应Bayes优化的人工免疫二进制PSO算法,融合人脸的局部和全局特征进行身份识别,显著提高了人脸识别性能。同时,融合人脸与指纹特征进行身份识别,也获得了较好的性能。
As a novel personal identification technology using certain physiological or behavioral traits associated with the person, Biometrics has always been an attractive topic of research. Biometric systems make use of fingerprints, hand geometry, iris, retina, face, hand vein, facial thermograms, signature or voiceprint to verify a person's identity. They have an edge over traditional security methods in that they cannot be easily stolen or shared. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones and ATMs.
Neural networks approach has been regarded as an interesting and powerful tool for biometrics identification. Since 1980s, more and more experts from some famous universities such as MIT and Harvard universities have been doing research on the theories and the applications of neural networks. Many important achievements have been reported on some first class international journals such as Science and Nature.
This thesis studies the problem of multi-modal biometric fusion by using neural networks. The main contributions of this thesis are as follows:
1. Using neural networks to study holistic and partial based face recognition. It combines Neural Networks, Principal Component Analysis (PCA), Non-negative Matrix Factorization with Sparseness constraints (NMFs), Radial Basis Function (RBF), Fisher's Linear Discriminant (LDA or FLD), to investigate face recognition for face images with large variations in lighting, pose, facial expression and partial occlusion noise or partially damaged facial images. A novel partial facial features extracting method by combining NMFs with FLD (FNMFs) is proposed, and the RBF classifier is then applied to classify the facial images with large variations. A comparative analysis engages PCA-FLD (FPCA) method and FNMFs method for both parts-based and holistic-based face recognition. The comparative experiments show that FNMFs has better performance than FPCA-based method for face recognition.
2. Using subspace associative memory with continuous attractors to study face recognition. The traditional associative memories with fixed-point attractors and associative memories with continuous attractors are studied. A subspace associative memory with continuous attractors is proposed. It is applied to recognize partially damaged or occluded facial images. The theoretical expressions are plotted, and the comparative experiments are carried out. It shows that partial-feature-based subspace associative memory outperforms holistic-feature-based subspace method significantly in recognizing partially damaged faces, and the subspace associative memory can learn and store some continuous attractors for completion partially damaged face images.
3. Some algorithms are proposed on continuous and discrete binary particle swarm optimization (PSO). In these algorithms, both maximum and minimum velocities are controlled to improve the abilities of the convergence by applying the theory of negative selection in Artificial Immune System (AIS). Two multilayer perceptron networks are successfully trained by the PSO with minimum and maximum velocity constraints in order to overcome premature convergence and alleviate the influence of dimensionality increasing.
4. A novel binary PSO algorithm based on Adaptive Neuro-Fuzzy Inference System and Artificial Immune System for face recognition is proposed to select the fusion rules by minimizing the Bayesian error cost. Such fusion rules are applied to face recognition as well as fusion face and fingerprint. Experimental results show that the proposed fusion algorithm outperforms individual algorithms that based on PCA or NMFs.
引文
[1] A. Ross, A. Jain. Information fusion in biometrics. Pattern Recognition Letters, 2003,24(13):2115-2125.
[2] R. Cappelli, D. Maltoni. Combining fingerprint classifiers. First International Workshop on Multiple Classifier Systems, 2000, 351-361.
[3] A. K. Jain, S. Prabhakar, L. Hong. A multichannel approach to classification. IEEE Transactions on PAMI, 1999,21 (4): 348-359.
[4] Xiaorong Pu, Zhang Yi, Zhongjie Fang. Holistic and Partial Facial Features Fusion by Binary Particle Swarm Optimization. Neural Computing & Applications, 2007 (已接收).
[5] T. K. Ho, J. J. Hull, S. N. Srihari. Decision combination in multiple classifier systems. IEEE Transactions on PAMI, 1994,16 (1): 66-75.
[6] S. Prabhakar, A. K, Jain. Decision-level fusion in fingerprint verification. Pattern Recognition,2002,35(4): 861-874.
[7] A. K. Jain, S. Prabhakar, S. Chen. Combining multiple matchers for a high security fingerprint verification system. Pattern Recognition Letters, 1999,20: 1371-1379.
[8] R. Brunelli, D. Falavinga. Person Identification using Multiple Cues. IEEE Transaction Pattern Analysis andMachine Intelligence, 1995,17(10): 955-966.
[9] B. Duc, E .S. Bigun, J. Bigun, et al. Fusion of Audio and Video Information for Multi-Modal Person Authentication. Pattern Recognition Letters, 1997, 18: 835-843.
[10] P.Verlinde, G.Chollet. Combining Vocal and Visual Cues in Identity Verification System using K-NN based Classifiers. Proceedings of the IEEE Workshop on Multimedia signal processing, Los Angeles,CA,USA,1998.
[11] J. Kittler, M.Hatef, R.P.W.Duin, et al. On Combining Classifiers. IEEE Transaction Pattern Analysis and Machine Intelligence. 1998,20(3): 226-239.
[12] L.Hong, A.K.Jain. Integration Faces and Fingerprints for Personal Identification. IEEE Transaction Pattern Analysis and Machine Intelligence, 1998,20(12): 1295-1300.
[13] L.Hong, A.KJain, S.Pankanti. Can Multibiometrics Improve Performance? Proceedings AutoID'99, 1999, 59-64.
[14]A.K.Jain,A.Ross.Leaming User-Specific Parameters in a Multibiometric System.Proceedings of the Ninth IEEE International Conference on Image Processing(ICIP),Rochester,New York,2002,22-25.
[15]A.K.Jain,R.P.W.Duin,J.Mao.Statistical Pattern Recognition:A Review.IEEE Transaction Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[16]S.Prabhakar,A.K.Jain.Decision Level Fusion in Fingerprint Verification.Pattern Recognition,2002,35(4):861-874.
[17]刘红毅,王蕴红,谭铁牛.基于改进ENN算法的多生物特征融合的身份验证.自动化学报,2004,30(1):78-85.
[18]Xiaorong Pu,Leiting Chen,Zhongjie Fang.PSO-Based Adaptive Person Identification with Face and Fingerprint Fusion.Dynamics of Continuous,Discrete and Impulsive Systems,International Journal for Theory and Applications(Series A),2007,14(SI):481-485.
[19]U.Dieckmann,P.Plankensteiner,T.Wagner,Sesam:A biometrie person identification system using sensor fusion.Pattern Recognition Letters,1997,18(9):827-833.
[20]J.Kittler,M.Hater,R.P.Duin,et al.On combining classifiers.IEEE Transactions on PAMI,1998,20(3):226-239.
[21]E.Bigun,J.Bigun,B.Due,et al.Expert conciliation for multimodal person anthentieation systems using Bayesian Statistics.First International Conference on AVBPA,Crans-Montana,Switzerland,1997,291-300.
[22]A.K.Jain,L.Hong,Y.Kulkami.A multimodal biometric system using fingerprint,face and speech.1999 Second International Conference on AVBPA,Washington D.C.,USA,182-187.
[23]P.Verlinde,G.Cholet.Comparing decision fusion paradigms using k-NN based classifiers,decision trees and logistic regression in a multi-modal identity verification application.1999 Second International Conference on AVBPA,Washington D.C.,USA,188-193.
[24]S.Ben-Yacoub,Y.Abdeljaoued,E.Mayoraz.Fusion of face and speech data for person identity verification.Research Paper IDIAP-RR 99-03,IDIAP,CP 592,1920 Martigny,Switzerland,1999.
[25]A.K.Jain,S.Prabhakar,A.Ross.Fingerprint matching:Data acquisition and performance evaluation.Technical Report MSU-TR:99-14,Michigan State University,1999.
[26]Y.Zuev,S.Ivanon.The voting as a way to increase the decision reliability.Foundations of Information/Decision Fusion with Applications to Engineering Problems,Washington D.C.,USA,1996,pp.206-210.
[27]K.Veeramachaneni,L.Osadciw,P.Varshney.An Adaptive Multimodal Biometric Management Algorithm.IEEE Trans.on Systems,Man,and Cybernetics:part C:Applications and Reviews,2005,35(3):344-356.
[28]Lee D D and Senng H S.Learning the parts of objects by non-negative matrix factorization.Nature,1999,401(6755):788-791
[29]Turk M A.,Pentland A.Eigenfaces for recognition.Journal of Cognitive.Neurosience,1991,3:71-86
[30]Patrik O H.Non-negative Matrix Factorization with Sparseness Constraints.Journal of Machine Learning Research,2004,5:1457-1469.
[31]边肇祺,张学工.模式识别.清华大学出版社,2004.
[32]Sirovich L,Kirby M.Low-dimensional procedure for the characterization of human faces.Opt.soc.Am.A.1987,4(3):519-524.
[33]Kirby M,Sirovich L.Application of the Karhunen-Loeve Procedure for the Charaterization of Human Faces.IEEE Trans.on Pattern Analysis and Machine Intelligence,1990,12(1):103-108.
[34]Praveen Sankaran,Vijayan Asari.A Multi-View Approach on Modular PCA for Illumination and Pose Invariant Face Recognition.2004 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04),165-170.
[35]Alex Pentland,Baback Moghaddam,Thad Starner.View-based and Modular Eigenfaces for face Recognition.1994 IEEE Conference on Computer Vision & Pattern Recognition,84-91.
[36]J Yang,D Zhang,A F Frangi,et al.Two-dimensional PCA:a new approach to appearance-based face representation and recognion.IEEE Trans PAMI,2004,26(1):131-137.
[37]M.H.Yang,N.Ahuja,D.Kriegman.Face recognition using kernel eigenfaces.Proc.of.Int.Conf.Image Processing,Vancouver,Canada,2000,1:37-40.
[38]Rajkiran G,Asari Vijayan K.An improved face recognition technique based on modular PCA approach.Pattern Recognition Letters,2004,25(4):429-436.
[39]Tan K.R,Chen S.C.Adaptively weighted sub-pattern PCA for face recognition.Neurocomputing,2005,64:505-511.
[40]Albert Pujol,Jordi Vitrià,Felipe Lumbreras,et al.Topological principal component analysis for face encoding and recognition.Pattern Recognition Letters,2001,22:769-776.
[41]S.Chen.Nonlinear Time Series Modeling and Prediction Using Gaussian RBF Networks with Enhanced Clusting and RLS Learning.Electronics Letters,1995,31(2):117-118.
[42]V TElanayar,Y C.Shin.Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems.IEEE Trans.Neural Networks.1994,7(5):594-603.
[43]S.G.Fabri,V Kadirkamanathan.Dynamic Structure Neural Networks for Stable Adaptive Control of Nonlinear Systems.IEEE Trans.Neural Networks,1996,7(5):1151-1167.
[44]D.L.Yu,J.B.Gomm,D.Willians.Sensor Fault Diagnosis in a Chemical Process via RBF Neural Networks.Control Engineering Practice.1999,7:49-55.
[45]Q.Zhao,Z.Bao.Target recognition based on radial basis function network.In:Processing of International Joint Conference on Neural Network,Japan,1993,3:2735-2738.
[46]W.Pedrycz.Conditional fuzzy clustering in the design of radial basis function neural networks.IEEE Trans.Neural Networks.1998,9:601-612.
[47]D.Lowe.Adaptive Radial Basis Function Nonlinearities and the Problem of Generalization.Proc.1IEE ConfArfificial Neural Networks,London,UK,1989,171-175.
[48]D.S.Broomhead,D.Lowe.Multivariable Functional Interpolation and Adaptive Networks.Complex Syst,1988,2:321-355.
[49]S.Chen,C.E N.Cowan,P.M.Grant.Orthogonal Least Square Learning Algorithm for Radial Basis Function Networks.IEEE Trans.Neural Networks.1991,2:302-309.
[50]S.Chen.Orthogonal Least Square Method and Their Application to Non-Linear System Identification.Int.J.Control.1989,50(5):1873-1896.
[51]T.Poggio,E Girosi.Networks for Approximation and Learning.Proceedings of the IEEE 1990,78(9):1481-1497.
[52]N.B.Karayiannis.Reformulated Radial Basis Neural Networks Trained by Gradient Descent.IEEE Trans.Neural Networks,1999,10(3):1999.
[53]J.E.Moody,C.J.Darken.Fast Learning in Networks of Locally Tunned Processing Units.Neural Computation.1989,1:281-294.
[54]Z.Uykan,H.N.Koivo.Augmented-input-layer Radial Basis Function Networks.In Proc ICSCAFAC Symp.Neural Computation.NC'98,Sept.1998,989-994.
[55]刘青山,卢汉青,马颂德.综述人脸识别中的子空间方法.自动化学报,2003,29(6):900-911
[56]R.A.Fisher.The Use of Multiple Measures in Taxonomic Problems.Ann.Eugenics,1936,7:179-188
[57]J.W.Lu,K.N.Plataniotis,A.N.Venetsanopoulos.Face Recognition Using Kemel Direct Discriminant Analysis Algorithms.IEEE Trans.on Neural Networks,2003,14(1):117-126
[58]P.N.Belhumeur,J.P.Hespanha,D.J.Kriegman.Eigenfaces vs.Fisherfaces:Recognition Using Class Specific Linear Projection.IEEE Trans.Pattern Anal.Machine Intell,1997,19(7):711-720.
[59]Xiaorong Pu,Zhang Yi,et al.,Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints.Lecture Notes in Computer Science,2005,LNCS 3497:112-117.
[60]蒲晓蓉,郑自明,周伟.基于子空间投影的残缺人脸图像识别新算法.电子科技大学学报,2006,35(2):208-220.
[61]Wei Zhou,Xiaorong Pu,Ziming Zheng.Parts-based or Holistic Face Recognition with RBF Neural Networks.Lecture Notes in Computer Science,2006,LNCS 3972:110-115.
[62]C.L.Chen,W.C.Chen,F.Y Chang.Hybrid Learning Algorithm for Gaussian Potential Function Networks.IEE Proceedings:Control Theory and Applications,1993,140(6):442-448.
[63]C.M.Bishop.Neural Networks for Pattern Recognition,New York:Oxford Univ.Press,1995.
[64]G.Bors,M.Gabbouj.Minimal topology for a radial basis functions neural networks for pattern classification.Digital Processing,1994,4:173-188.
[65]UMIST人脸数据库:http://images.ee.umist.ac.uk/danny/database.html
[66]ORL人脸数据库:http://www.cam-orl.co.uk/facedatabase-html
[67]D.Valentin,H.Abdi,A.O'Toole.Categorization and identification of human face images by neural networks:A review of the linear autoassociative and principal component approaches.Journal of Biological Systems,1994,2(3):413-429.
[68]M.T.Hagan,H.B.demuth,M.Beale.Neural Network Design.PWS Publishing Company.1996.
[69]Hopfield J J.Neurons with graded response have collective computational properties like those of two-state neurons.Proc Natl Acad Sci,USA.1984,81(10):3088-3092.
[70]Kosko B.Adaptive bidirectional associative memories.Appl.Opt.,1987,26(23):4947-4960.
[71]Costantini G.,Casali D.,Perfetti R.Neural associative memory storing gray-coded gray-scale images.IEEE Transactions on Neural Networks,2003,14(3):703-707
[72]S.Jankowski,A.Lozowski,J.M.Zurada.Complex-valued multistate neural associative memory.IEEE Trans.Neural Networks,7(6):1491-1496.
[73]M(u｜¨)ezzinoglu M.K.,G(u｜¨)zelis C.,Zurada J.M.ANew Design Method for Complex-Valued Multistate Hopfield Associative Memory.IEEE Transactions on Neural Networks.2003,14(4):890-891.
[74]Surina I.I.On a continuous-type associative memory.Neuroinformatics and Neurocomputers,1992,7(10):784-792.
[75]H.J.Tang,K.C.Tan,Zhang Yi.Neural Networks:Computational Models and Applications.Springer,Berlin Heidelberg New York.2007.
[76]Seung H.S.Learning continuous attractors in recurrent networks.Advances in Neural Information Processing Systems.10:654-660.
[77]H.S.Seung.Learning continuous attractors in recurrent networks.http://hebb.mit.edu/people/seung/papers/continuous.abstract Adv.Neural Info.Proc.Syst.1998,10:654-60.
[78]H.S.Seung.Pattern analysis and synthesis in attractor neural networks.In Theoretical Aspects of Neural Computation:A Multidisciplinary Perspective,Proceedings of TANC'97.Springer-Verlag.1997
[79]Xiaorong Pu,Zhang Yi.Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories.Lecture Notes in Computer Science,2006,LNCS 3972:49-54.2006
[80]C.Reynolds.Flocks,birds and schools:a distributed behavioral model.Computer Graphics,1987,21(1):25-34.
[81]Heppner F,Grenander U.A stochastic nonlinear model for coordinated bird flocks.The Ubiquity of Chaos.Washington DC:AAAS,1990.
[82]D.E.Goldberg.Genetic algorithms in search:optimization and machine learning.Addsion-Weslesy,1989.
[83]Dorigo M,Di Caro G.Ant Colony Optimization:A New Meta-Heuristic.Proceedings of the Evolutionary Computation,Washington DC.1999:1470-1477
[84]Kennedy,J.and Eberhart,R.C.Particle swarm optimization.Proc.IEEE Int'l.Conf.on Neural Networks,Ⅳ,1942-1948.Piscataway,NJ:IEEE Service Center,1995.
[85]J.Tillet,R.Rao,F.Sahin,et al.Cluster-head Identification in Ad Hoe Sensor Networks Using Particle Swarm Optimization.Proceedings of the IEEE International Conference on Personal Wireless Communication.2002.
[86] R. Muraleedharan, L. A. Osadciw. Balancing the Performance of a Sensor Network Using an Ant System. Proceedings of CISS, March 12-14,2003, Baltimore, Maryland.
[87] T. Back T. Evolutionary algorithm in theory and practice. New York: Oxford University Press, 1996.
[88] Orosz J.E., Jacobson S.H. Analysis of Static Simulated Annealing Algorithms. Journal of Optimization Theory and Application, 2002, 115(1): 165-182.
[89] Kennedy J, Mendes R. Population structure and particle swarm performance. Proceeding of IEEE conference on Evolutionary Computation, 2002, 1671-1676.
[90] Anthony Carlisle, Gerry Dozier. An Off-The-Shelf PSO. PSO Workshop IUPUI, Indianapolis,April, 2001.
[91] Shi. Y, R. C. Eberhart. Empirical Study of Particle Swarm Optimization. 1999 Congress on Evolutionary Computing. III: 1945-1950.
[92] Kennedy J, Eberhart R C.A discrete binary version of the particle swarm algorithm. Proceedings of 1997 IEEE International Conference on Systems, Man and Cybernetics, 1997,5:41044108.
[93] F. Afshinmanesh, A. Marandi, A. Rahimi-Kian. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System. Proc. of IEEE International Conference on Computer as a Tool (EUROCON 2005).
[94] Clerc M., Kennedy J. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. 2002, 6(1):58-73.
[95] Liu H, Abraham A. Fuzzy Turbulent Particle Swarm Optimization. Proceeding of the 5th International Conference on Hybrid Intelligent Systems, Brazil, IEEE CS Press, USA, 2005.
[96] H. Pohlheim. Genetic Algorithm Toolbox Test Functions. De-partment of Automatic Control and Systems Engineering University of Sheffield, 2005.
[97] Yasuda K., Iwasaki N. Adaptive particle swarm optimization using velocity information of swarm. Proceedings of IEEE International Conference on Systems, Man and Cybernetics. 2004,3475-3481.
[98] Xiaorong Pu, Zhongjie Fang, Yongguo Liu. Multilayer Perceptron Networks Training using Particle Swarm Optimization with Minimum Velocity Constraints. Lecture Notes in Computer Science, 2007, LNCS 4493: 237-245.
[99] L.A.Zadeh. Fuzzy Sets. Information and Control, 1965, 8:338-353.
[100] J. S. R. Jang. ANFIS: Adaptive network based fuzzy inference system. IEEE Trans.Syst., Man,Cybern. 1993,23(3): 65-68.
[101] Y. Fang, T. Tan, Y. Wang. Fusion of Global and Local Features for Face Verification. Pattern Recognition. 2002,12: 382-385
[102] J. Shen, et al. Fuzzy Neural Nets with Non-symmetric Membership Functions and Applications in Signal Processing and Image Analysis. Singal Processing. 2000, 80: 965-983.
[103] Robert W. Frischholz, Ulrich Deickmann. BioID: AMultimodal Biometric Identification System. IEEE Computer. 2000, 33(2):.64-68
[104] S. Prabhakar, A. Jain. Decision-level Fusion in Fingerprint Verification. Pattern Recognition.2002, 35: 861-874.
[105] S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Vol. II,Prentice-Hall, Inc., 1998.
[106] R. Viswanathan, P. K. Varshney. Distributed Detection with Multiple Sensors: Part I -Fundamentals. Proceedings of the IEEE. 1997, 85(1): 54 - 63.
[107] Pramod K. Varshney. Distributed Detection and Data Fusion. Springer, New York, 1997.
[108] Artiklar M., Hassoun M., Atta P. Application of a Postprocessing Algorithm for Improved Human Face Recognition. Proceedings of the IEEE International Conference on Neural Networks,IJCNN-1999, July 10-16, 1999, Washington, DC., Paper#JCNN 2166.
[109] Ziming Zheng, Xiaorong Pu, Zhang Yi. Fusion of PCA and NMFS for Face Recognition. 2007 (Submitted).
[110] Mu X., Artiklar M., Artiklar M.,et al. Training algorithms for robust face recognition using a template-matching approach. Proceedings of the International Joint Conference on Neural Networks.2001,4:2877-2882.
[111] Bruce V. Recognizing faces. Lawrence Erlbaum Associates, London, U.K. 1988.
[112] Bruce V., Hancock P. J. B., Burton A.M. Human face perception and identification. In Face Recognition: From Theory to Applications,H. Wechsler, P. J. Phillips, V. Bruce, F. F. Soulie, and T.S. Huang, Eds. Springer-Verlag, Berlin, Germany, 51-72. 1998.
[113] W. Zhao, R. Chellappa, A. Rosenfeld, et al. Face recognition: A literature survey. ACM Computing Surveys, 2003, 35(4): 399-458.
[114] A. K. Jain, A. Ross. Learning User-specific Parameters in a Multibiometric System. Proc.International Conference on Image Processing (ICIP), pp. 57-60, Rochester, New York, September 22-25,2002.
[115] Ratha N, Chen S, Jain A K. Adaptive flow orientation based feature extraction in fingerprint images. Pattern Recognition, 1995,28(11): 1657-1672.
[116] Maio D, Maltioni D. Direct gray-scale minutiae detection in fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997,19(1): 27-40.
[117] Dario Maio, Direct Gray-Scale Minutiae Detection in Fingerprints. IEEE Trans on Pattern Analysis and Machine Intelligence. 1997,1(19): 27-40.
[118] Lee H. C, Gaensslen R E. Advances in Fingerprint Technology. New York: Elsevier,1991.
[119] Lawrence O' Gorman, Feffref V. Nickersom. An approach to fingerprint filter design. Pattern Recognition, 1989,22(1): 29-38.
[120] Jain K, Prabhakar S, Lin H, et al. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing. 2000,9(5): 846-859.
[121] Kenneth Nilsson, Josef Bign. Localization of corresponding points in fingerprints by complex filtering. Pattern Recognition Letters, 2003,24(13): 2135-2144.
[122] J.GDaugman. High confidence recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Machine Intell. 1993, 15(11): 1148-1161.
[123] A. K. Jain, S. Prabhakar, L. Hong. A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Machine Intell. 1999, 21(4): 348-359.
[124] J. Ortega-Garcia, J. Fierrez-Aguilar, D. Simon, et al. MCYT baseline corpus: abimodal biometric database, IEE Proc.-Vis. Image Signal Process. 2003, 150(6).