三维人脸识别及其模板保护算法研究
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摘要
身份认证是计算机和互联网世界里最基本的一个要素,也是整个信息安全体系的基础。生物识别技术应用于身份认证是未来发展的趋势,相对于基于口令和智能卡的技术来说,生物识别技术不仅能够为用户提供安全等级更高的保护,而且无需记忆、不易丢失,在使用上更加方便。
随着人们安全意识的不断加强,生物识别系统中固有的安全漏洞已受到广泛关注。目前绝大部分的安全威胁,来自于生物识别系统中直接存储模板这一传统的工作方式。生物模板保护是近年来专门为解决生物模板存储的安全问题而提出的一项新技术,它将模板转换为一种秘密的形式进行存储,在实现身份认证的同时,不泄露原始模板的信息。作为一门新兴的技术,模板保护受到越来越多的关注,但总的说来,在理论和实际应用中仍然存在诸多的问题。
生物模板保护技术在实现对模板安全保护的同时,也对现有的一些生物识别技术造成一定的冲击。由于模板保护技术不直接存储模板信息,使得一些现有的成熟的算法不再适用。其中,三维人脸识别技术所受到的影响是最大的。本文将模板保护技术应用到三维人脸识别技术中,在模板保护的约束条件下,开发相应的三维人脸识别技术。针对高、低维不同的三维特征模板形式,分别提出基于密钥生成原则的和基于密钥绑定原则的模板保护算法,并对两个算法在理论上的安全性和实际应用中的安全性进行了详细的分析。论文的主要创造性工作如下:
提出适用于模板保护的三维人脸预处理方法。该方法可在没有原始模型参与的情况下,实现三维人脸模型自动、高效和精确地预处理。本文针对的是最原始的三维点云人脸结构,在没有拓扑信息的支持下,通过分析三维数据的分布特征,提出头肩分离的方法,结合精确定位的人脸基准点以及五官分布特点,实现对人脸区域的准确裁减;最后通过鼻梁骨在三个平面的投影角度对人脸进行配准。仿真实验证明该方案能够精确的定位人脸特征点,其配准精度与经典的ICP方法相当,而速度远远快于ICP方法。
提出了两种多模态信息融合的识别算法,一种是直方图局部统计特征提取算法,它通过分层的办法,在每个层内包含的三维人脸有效点中,提取点数量和能量的直方图系数作为特征,该方法能够快速有效地对三维人脸进行识别验证;另外一种是多模态信息融合的整体统计特征提取算法,它通过对三维模型的重采样,并融合灰度、曲率和深度信息,用模式识别的方法提取整体的特征,仿真实验表明该方法具有很高的识别性能。
针对高维特征模板,设计了基于密钥生成原则的模板保护算法–自伪装方案(SMS: Self-masked scheme)。该算法能够通过调节量化参数灵活地在安全和识别性能之间进行权衡,将其应用到直方图局部统计特征模板保护中,从信息论的角度对算法的安全性进行分析,提出了安全性能和识别性能综合分析的方法,并结合实验结果对算法在具体使用中的安全性进行定量分析。
针对低维特征模板,设计了基于密钥绑定原则的模板保护算法–秘密分享模板(SST: Secret Share Template)。该方案采用秘密分享技术,设计双重容错机制提高容错性,提出以时间效率换取较高的安全性和识别性,将其应用到多模态信息融合的整体统计特征模板保护中,实验表明,该方案优于目前所知的所有三维人脸识别算法。
Identity authentication is the most important basis of information security system.Compared with passwords and smart cards, biometric identification is unforgettable andnot easy to lose, which affords users higher level security protection. Thus, biometricidentification has become the trend for identity authentication.
Nowadays, the security vulnerability of existing biometric identification technologyhas drawn more and more attention of researchers. Most of the security threats are causedby traditional direct storage templates.he technology of biometric template protection hasemerged in recent years, which turns the traditional template into a secret style to store.As such, there will be no information leakage of the original templates during identityauthentication. However, there still exist many theoretical and application problems.
While biometric template protection technology ensures the security of templates, itbrings great challenges to existing identification technologies. As the template informa-tion is not directly stored, most existing 3D face recognition algorithms cannot work dueto the restrictions for registration, feature extraction and feature matching.
In this thesis, we adopt the template protection technology to 3D face recognitionunder restrictions. According to different styles of 3D face templates, we proposed twotemplate protection algorithms based on hamming-distance space and sequence-distancespace respectively. We analyze the theoretical and application security of the algorithmsin detail. The contributions of our thesis are as follows:
We have proposed a 3D face preprocessing method for template protection. Ourmethod could preprocess information efficiently, accurately, and automatically. Ourmethod works on the original 3D face points-cloud model without topological informa-tion, which isolates the head from shoulder by analyzing the 3D data distribution. Com-bined with the accurate location of some basic feature points and distribution of five senseorgans, the face area is accurately segmented. At last, the face pose is corrected accordingto the three projection angles of the nose bridge on the three facets. The effectiveness ofour preprocessing method is shown through the simulation results.
We have proposed two typically feature extraction algorithms for template protec-tion. The first algorithm is based on local statistic features, containing the histogram values of point number and energy that are extracted from the divided horizontal layer.The second one is based on global features using space integration. We resample the 3Dface model and integrate the gray value, curve rate and depth value into a new feature, anduse pattern recognition to train the feature. The two algorithms are proved to be highlyeffective for recognition.
As for the long feature templates, we have designed a template protection algorithmbased on key-generating-SMS (self-masked scheme) and applied it in the feature extrac-tion algorithm based on local statistic feature template protection. We analyze the securityof our algorithm in the view of information theory and propose a comprehensive analy-sis method of security and recognition performance. Finally, we provide a quantitativeanalysis of the algorithm in specific application with experiment results.
As for the long feature templates, we have designed a template protection algorithmbased on key-binding-SST (secret share template ). Secret sharing technology is adoptedand double error tolerances mechanisms are designed to improve the error tolerancescapacity in SST. High security level and recognition performance can be achieved at thecost of efficiency in SST. We applied the algorithm in the feature extraction algorithmbased on global features using space integration. The result showed that SST scheme isthe best template protection algorithm.
随着人们安全意识的不断加强,生物识别系统中固有的安全漏洞已受到广泛关注。目前绝大部分的安全威胁,来自于生物识别系统中直接存储模板这一传统的工作方式。生物模板保护是近年来专门为解决生物模板存储的安全问题而提出的一项新技术,它将模板转换为一种秘密的形式进行存储,在实现身份认证的同时,不泄露原始模板的信息。作为一门新兴的技术,模板保护受到越来越多的关注,但总的说来,在理论和实际应用中仍然存在诸多的问题。
生物模板保护技术在实现对模板安全保护的同时,也对现有的一些生物识别技术造成一定的冲击。由于模板保护技术不直接存储模板信息,使得一些现有的成熟的算法不再适用。其中,三维人脸识别技术所受到的影响是最大的。本文将模板保护技术应用到三维人脸识别技术中,在模板保护的约束条件下,开发相应的三维人脸识别技术。针对高、低维不同的三维特征模板形式,分别提出基于密钥生成原则的和基于密钥绑定原则的模板保护算法,并对两个算法在理论上的安全性和实际应用中的安全性进行了详细的分析。论文的主要创造性工作如下:
提出适用于模板保护的三维人脸预处理方法。该方法可在没有原始模型参与的情况下,实现三维人脸模型自动、高效和精确地预处理。本文针对的是最原始的三维点云人脸结构,在没有拓扑信息的支持下,通过分析三维数据的分布特征,提出头肩分离的方法,结合精确定位的人脸基准点以及五官分布特点,实现对人脸区域的准确裁减;最后通过鼻梁骨在三个平面的投影角度对人脸进行配准。仿真实验证明该方案能够精确的定位人脸特征点,其配准精度与经典的ICP方法相当,而速度远远快于ICP方法。
提出了两种多模态信息融合的识别算法,一种是直方图局部统计特征提取算法,它通过分层的办法,在每个层内包含的三维人脸有效点中,提取点数量和能量的直方图系数作为特征,该方法能够快速有效地对三维人脸进行识别验证;另外一种是多模态信息融合的整体统计特征提取算法,它通过对三维模型的重采样,并融合灰度、曲率和深度信息,用模式识别的方法提取整体的特征,仿真实验表明该方法具有很高的识别性能。
针对高维特征模板,设计了基于密钥生成原则的模板保护算法–自伪装方案(SMS: Self-masked scheme)。该算法能够通过调节量化参数灵活地在安全和识别性能之间进行权衡,将其应用到直方图局部统计特征模板保护中,从信息论的角度对算法的安全性进行分析,提出了安全性能和识别性能综合分析的方法,并结合实验结果对算法在具体使用中的安全性进行定量分析。
针对低维特征模板,设计了基于密钥绑定原则的模板保护算法–秘密分享模板(SST: Secret Share Template)。该方案采用秘密分享技术,设计双重容错机制提高容错性,提出以时间效率换取较高的安全性和识别性,将其应用到多模态信息融合的整体统计特征模板保护中,实验表明,该方案优于目前所知的所有三维人脸识别算法。
Identity authentication is the most important basis of information security system.Compared with passwords and smart cards, biometric identification is unforgettable andnot easy to lose, which affords users higher level security protection. Thus, biometricidentification has become the trend for identity authentication.
Nowadays, the security vulnerability of existing biometric identification technologyhas drawn more and more attention of researchers. Most of the security threats are causedby traditional direct storage templates.he technology of biometric template protection hasemerged in recent years, which turns the traditional template into a secret style to store.As such, there will be no information leakage of the original templates during identityauthentication. However, there still exist many theoretical and application problems.
While biometric template protection technology ensures the security of templates, itbrings great challenges to existing identification technologies. As the template informa-tion is not directly stored, most existing 3D face recognition algorithms cannot work dueto the restrictions for registration, feature extraction and feature matching.
In this thesis, we adopt the template protection technology to 3D face recognitionunder restrictions. According to different styles of 3D face templates, we proposed twotemplate protection algorithms based on hamming-distance space and sequence-distancespace respectively. We analyze the theoretical and application security of the algorithmsin detail. The contributions of our thesis are as follows:
We have proposed a 3D face preprocessing method for template protection. Ourmethod could preprocess information efficiently, accurately, and automatically. Ourmethod works on the original 3D face points-cloud model without topological informa-tion, which isolates the head from shoulder by analyzing the 3D data distribution. Com-bined with the accurate location of some basic feature points and distribution of five senseorgans, the face area is accurately segmented. At last, the face pose is corrected accordingto the three projection angles of the nose bridge on the three facets. The effectiveness ofour preprocessing method is shown through the simulation results.
We have proposed two typically feature extraction algorithms for template protec-tion. The first algorithm is based on local statistic features, containing the histogram values of point number and energy that are extracted from the divided horizontal layer.The second one is based on global features using space integration. We resample the 3Dface model and integrate the gray value, curve rate and depth value into a new feature, anduse pattern recognition to train the feature. The two algorithms are proved to be highlyeffective for recognition.
As for the long feature templates, we have designed a template protection algorithmbased on key-generating-SMS (self-masked scheme) and applied it in the feature extrac-tion algorithm based on local statistic feature template protection. We analyze the securityof our algorithm in the view of information theory and propose a comprehensive analy-sis method of security and recognition performance. Finally, we provide a quantitativeanalysis of the algorithm in specific application with experiment results.
As for the long feature templates, we have designed a template protection algorithmbased on key-binding-SST (secret share template ). Secret sharing technology is adoptedand double error tolerances mechanisms are designed to improve the error tolerancescapacity in SST. High security level and recognition performance can be achieved at thecost of efficiency in SST. We applied the algorithm in the feature extraction algorithmbased on global features using space integration. The result showed that SST scheme isthe best template protection algorithm.
引文
1 D. Maltoni, D. Maio, A. Jain, et al. Handbook of Fingerprintrecognition[M]. 2nded., New York: Springer-Verlag, 2003:10– 20.
2 K. Jain, R. Bolle, S. Pankanti. Personal Identification Innetworked Society[M].Norwell: Kluwer Academic, 1999:50– 70.
3 M. Jafar, A. Hassanien. An Iris Recognition System to Enhancee-security Environ-ment Based on Wavelet Theory[J]. Advanced Modeling and Optimization, 2003,2(5):93– 104.
4 G. Gordon. Face Recognition Based on Depth and Curvature Fea-tures[C]//Proceedings of IEEE Computer Society Conference on Computer Visionand Pattern Recognition (CVPR’92). Champaign, USA, 1992:808– 810.
5 C. Hesher, A. Srivastava, G. Erlebacher. A Novel Technique for Face Recogni-tion Using Range Images[C]//Proceedings of International Symposium on SignalProcessing and Its Applications. Paris, France, 2003:201– 204.
6 G. Medioni, R. Waupotitsch. Face Recognition and Modeling in 3D[C]//IEEE In-ternational Workshop on Analysis and modeling of Faces and Gestures (AMFG2003). Nice, France, 2003:232– 223.
7 P. Phillips, P. Flynn, T. Scruggs, et al. Overview of the Face Recognition GrandChallenge[C]//Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition. San Diego, USA, 2005:947– 951.
8 C. Soutar, D. Roberge, A. Stoianov, et al. Method for Secure Key ManagementUsing a Biometric[J]. U.S. Patent, 2001, (6219794).
9 T. Matsumoto, H. Matsumoto, K. Yamada, et al. Impact of Artificial Gummy Fin-gers on Fingerprint Systems[C]//Proceedings of SPIE conference on Optical Secu-rity and Counterfeit Deterrence Techniques IV. San Jose, USA, 2002.
10王跃明,潘纲,吴朝晖.三维人脸识别综述[J].计算机辅助设计与图形学学报, 2008, 20(7):819– 829.
11刘向东.全自动三维人脸识别框架[D].吉林:吉林大学, 2007:1– 5.
12 K. Chang, K. Bowyer, P. Flynn. Face Recognition Using 2D and 3D Fa-cial Data[C]//Multimodal User Authentication WorkShop. Santa Barbara, USA,2003:25– 32.
13 S. Malassiotis, M. Strintzis. Real Time Head Tracking and 3D Pose Estimationfrom Range Data[C]//Processings of International Conference on Image (ICIP’03).Barcelona, Spain, 2003.
14 D. A. Field. Laplacian Smoothing and Delaunay Triangulations[J]. Communica-tions in Applied Numerical Methods, 1988, 4(6):709– 712.
15 G. Taubin. A Signal Processing Approach to Fair Surface Design[C]//Proceedingsof the 22nd annual conference on Computer graphics and interactive techniques.New York, USA, 1995:351– 358.
16 G. Taubin, T. Zhang, G. Golub. Optimal Surface Smoothing as FilterDesign[C]//Proceedings of the 4th European Conference on Computer Vi-sion(ECCV’96). Cambridge, UK, 1996:283– 292.
17中煜湘,邹北骥,彭永进.三基于面分块的三维人脸模型的边界表示法[J].湖南大学学报, 2001, 5(28):112– 117.
18 T. Fujiwara, T. Nishihara, M. Tominaga, et al. On the Detection of Feature Pointsof 3D Facial Image and its Application to 3D Facial Caricature[C]//Second Inter-national Conference on 3D Imaging and Modeling (3DIM’99). Ottawa, Canada,1999:4– 9.
19 C. Dorai, A. Jain. Cosmos: A Representation Scheme for 3D Free-form Ob-jects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(10):1115– 1130.
20 C. Chua, R. Jarvis. Point Signayure: A New Representation for 3D Object Recog-nition[J]. International Journal on Computer Vision, 1997, 1(25):63– 85.
21 Y. Wang, C.-S. Chua, Y.-K. Ho. Facial Feature Detection and Face Recognitionfrom 2D and 3D Images[J]. Pattern Recognition Letters, 2002, 10(23):1191– 1202.
22 X. Lu, D. Colbry, A. Jain. Three Dimensional Model Based Face Recogni-tion[C]//Proceedings of International Conference Pattern Recognition (ICPR’04).Cambridge, USA, 2004:362– 366.
23 L. Wiskott, J.-M. Fellous, et al. Face Recognition by Elastic Bunch Graph Match-ing[J]. IEEE Transactions on pattern analysis and maching intelligence, 1997,7(19):775– 779.
24 A. B. Moreno, A. Sanchez, J. F. Velez, et al. Face Recognition Using 3d Surface-extracted Descriptors[C]//Proceedings of Irish maching vision and image process-ing(IMVIP’03). Maynooth, Ireland, 2003:50– 57.
25 D. Huber, M. Hebert. Fully Automatic Registration of Multiple 3D Data Sets UsingGaussian Fields[J]. Image and Vision Computing, 2003, 21(7):637– 650.
26 B. K. P. Horn. Closed Form Solution of Absolute Orientation Using Unit Quater-nions[J]. Journal of the Optical Society of America, 1987, 4(4):629– 642.
27 P. Besl, N. McKay. A Method for Registration of 3D Shapes[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1992, 2(14):239– 256.
28 F. Mokhtarian. A Theory of Multiscale, Torsion Based Shape Representation[J].Computer vision and image understanding, 1997, 68(1):1– 17.
29 J. Feldmar, N. Ayache, F. Betting. 3D-2D Projective Registration of Free-form Curves and Surfaces[J]. Computer vision and image understanding, 1997,65(3):403–424.
30 K. S. Arun, T. S. Huang, S. Blostein. Least Square Fitting of Two 3D Point Sets[J].IEEE Transactions on Pattern Analysis and Maching Intelligence, 1987, 9(5):698–700.
31严庆光,李明哲,李东成.多点成形件检测中三维数据配准方法的研究[J].中国机械工程, 2003, 14(19):1648– 1651.
32彭育辉,高诚辉,何炳蔚.基于遗传算法和四元数法的曲面线扫描数据的配准[J].福州大学学报(自然科学版), 2008, 36(4):541– 546.
33 O. D. Faugeras, M. Hebert. The Representation, Recognition, and Locating of 3DObjects[J]. International Journal of Robotics Research, 1986, 5(3):27– 52.
34 O. D. Faugeras, M. Hebert. Using the Gaussian Image to Find the Orienttion of anObject[J]. International Journal of Robotics Research, 1983, 3(4):89– 125.
35 B. Horn. Extended Gaussian Images[J]. Proceedings of the IEEE, 1984,72(12):1656– 1678.
36 S. Rusinkiewicz, M. Levoy. Efficient Variants of the ICP Algo-rithm[C]//Proceedings of the International conference on 3D digital imagingand modeling(3DIM’01). Quebec, Canada, 2001:145– 152.
37 S. Rusinkiewicz, O. Hall-Holt, M. Levoy. Real-time 3D Model Acquisition[J].ACM Transactions on Graphics (TOG), 2002, 21(3):438– 446.
38 K. Chang, K. Bowyer, P. Flynn. Effects on Facial Expression in 3D Face Recogni-tion[C]//Proceedings of SPIE Conference Biometric Technology for Human Iden-tification. Orlando, USA, 2005:132– 143.
39 Y. Chen, G. Medioni. Object Modeling by Registration of Multiple Range Im-ages[J]. Image and vision computing, 1992, 10(3):145– 155.
40 T. Z. Jochen, J. Schmidt, H. Niemann. A Refined Icp Algorithm for Robust 3DCorrespondence Estimation[C]//Proceedings of International Conference on ImageProcessing (ICIP’03). Barcelona, Spain, 2003.
41 J. Cook, V. Ch, S. Sridharan, et al. Face Recognition from 3D Data Using IterativeClosest Point Algorithm and Gaussian Mixture Models[C]//Proceedings of the 2ndInternational Symposium on 3D Data Processing , Visualization and Transmission.Thessaloniki, Greece, 2004:502– 509.
42 X. Lu, A. Jain, D. Colbry. Matching 2.5D Face Scans to 3D Models[J]. IEEETransactions on Pattern Analysis and Machine Intelligence, 2006, 1(28):31– 43.
43 Y. Wang, G. Pan, Z. Wu, et al. Exploring Facial Expression Effects in 3D FaceRecognition Using Partial ICP[C]//Proceedings of the 7th Asian Conference onComputer Vision. Hyderabad, India, 2006:581– 590.
44 K. Chang, K. Bowyer, P. Flynn. Adaptive Rigid Multi-region Selection for Han-dling Expression Variation in 3D Face Recognition[C]//IEEE workshop on facerocognition grand challenge experiments. SanDiego, USA, 2005:157– 160.
45 B. Achermann, H. Bunke. Classifying Range Images of Human Faces with Haus-dorff Distance[C]//International Conference on Pattern Recognition (ICPR 2000).Barcelona, Spain, 2000:809– 813.
46 Y. Lee, J. Shim. Curvature-based Human Face Recognition Using Depth-weightedHausdorff Distance[C]//International Conference on Image Processing(ICIP’04).Singapore, 2004:1429– 1432.
47 T. Russ, K. Koch, C. Little. 3D Facial Recognition: A Quantitative Analy-sis[C]//38th Annual 2004 International Carnahan Conference on Security Technol-ogy. Albuquerque, USA, 2004:338– 344.
48 T. Russ, M. Koch, C. Little. A 2D Range Hausdorff Approach for 3D Face Recogni-tion[C]//IEEE Workshop on Face Recognition Grand Challenge Experiments. SanDiego, USA, 2005:169– 175.
49 T. K. Kim, S. C. Kee, S. R. Kim. Real-time Normalization and Feature Extractionof 3D Face Data Using Curvature Characteristics[C]//IEEE International workshopon robot and human interactive comminication. Paris, France, 2001:74– 79.
50 P. Yuen, F. Mokhtarian, N. Khalili, et al. Curvature and Torison Feature Extractionfrom Free-form 3D Meshs at Multiple Scales[J]. IEEE Proceedings vision, imageand signal processing, 2000, 147(5):454– 462.
51 M. Nomura, N. Hamada. Feature Edge Extraction from 3D Triangular MeshesUsing a Thinning Algorithm[J]. Institute of Electronics, Information and Commu-nication Engineers, 2001, 100(443):107– 118.
52孙海鹏,毕笃彦,陶杰.曲率及几何约束的三维人脸标志点定位[J].微计算机信息, 2009, 25(7):264– 266.
53 K. Chang, K. Bowyer, P. Flynn. Adaptive Rigid Multi-region Selection forHandling Expression Variation in 3D Face Recognition[C]//Proceedings of IEEEWorkshop on Face Recognition Grand Challenge Experiments. San Diego, USA,2005:157– 163.
54 Y. Lee, J. Shim. Curvature-based Human Face Recognition Using Depth WeightedHausdorff Distance[C]//Proceedings of International Conference on Image Process-ing. Singapore, 2004:1492– 1432.
55郭哲,张艳宁,林增刚.基于曲率信息的三维人脸面部特征提取[J].计算机工程与应用, 2007, 43(24):239– 245.
56 A. B. Moreno, A. Sanchez, J. Velez. Face Recognition Using 3D Surface-extractedDescriptors[C]//Proceedings of Irish Machine Vision and Image Processing. Ire-land, 2003:50– 57.
57 J. Lee, E. Milios. Matching Range Images of Human Faces[C]//Proceedings ofInternational Conference on Computer Vision. Osaka, Japan, 1990:722– 726.
58 A. Makadia, A. Patterson, K.Daniilidis. Fully Automatic Registration of 3d PointClouds[C]//IEEE Computer Society Conference on Computer Vision and PatternRecognition. New York, USA, 2006:1297– 1304.
59 H. T. Tanaka, M. Ikeda. Curvature-based Face Surface Recognition Using SphericalCorrelation-principal Directions for Curved Object Recognition[C]//Proceedings ofthe 3rd International Conference on Automated Face and Gesture Recognition. Vi-enna, Austria, 1998:372– 377.
60 G. Pan, Y. Wu, Z. Wu. Investigating Profile Extraction from Range Data for 3DRecognition[C]//Proceedings of IEEE International conference on multimedia &expo (ICME’04). Taipei, 2004:6– 9.
61 T. Nagamine, T. Uemura, I. Masuda. 3D Facial Image Analysis for Human Identifi-cation[C]//Proceedings of International Conference on Pattern Recognition. Hague,Netherlands, 1992:324– 327.
62 G. Pan, Z. Wu. 3D Face Recognition from Range Data[J]. International Journal ofImage and Graphics, 2005, 3(5):573– 593.
63 C. Chua, F. H. Y. Ho. 3D Human Face Recognition Using Point Signa-ture[C]//Proceedings of International Conference on Automatic Face and GestureRecognition. Grenoble, France, 2000:233– 238.
64 D. Colbry, G. Stockman. Canonical Face Depth Map : A Robust 3D Representationfor Face Verification[C]//Proceedings of IEEE Computer Society Conference onComputer Vision and Pattern Recognition. Minneapolis, USA, 2007:102– 109.
65 A. Bronstein, M. Bronstein, R. Kimmel. Three-dimensional Face Recognition[J].International Journal of Computer Vision, 2005, 1(64):5– 30.
66 A. Bronstein, M. Bronstein, R. Kimmel. Robust Expression Invariant Face Recog-nition from Partially Missing Data[C]//Proceedings of European Conference onComputer Vision. Graz, Austria, 2006:396– 408.
67 Y. Wang, G. Pan, Z. Wu. 3D Face Recognition in the Presence of Expression :A Guidance-based Const Raint Deformation Approach[C]//Proceedings of IEEEComputer Society Conference on Computer Vision and Pattern Recognition. Min-neapolis, USA, 2007:201– 207.
68 C. Xu, Y. Wang, T. Tan, et al. Automatic 3D Face Recognition Combining GlobalGeometric Features with Local Shape Variation Information[C]//Proceedings ofIEEE International Conference on Automatic Face and Gesture Recognition. Seoul,Korea, 2004:308– 313.
69 C. Zhong, Z. Sun, T. Tan. Robust 3D Face Recognition Using Learned VisualCodebook[C]//Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition. Minneapolis, USA, 2007:12– 17.
70 O. Chapelle, P. Haffner, V. Vapnik. Support Vector Machines for Histogram-basedImage Classification[J]. IEEE Transactions on neural networks, 1999, 10(5):1055– 1064.
71 T. Iwasa, T. Shima, M. Sai, et al. 3D Eigenfaces for Face Modeling[C]//Proceedingsof Asian Conference on Computer Vision, ACCV’02. Australia, 2002:308– 313.
72 J. Lei, C. Lu. 3D Eigenfaces for Face Modeling[C]//Proceedings of ACIS Inter-national Conference on Software Engineering, Artificial Intelligence, Networking,and Parallel/Distributed Computing (SNPD’07). Qingdao, China, 2007:501– 506.
73 C. Hesher, A. Srivastava, G. Erlebacher. A Novel Technique for Face Recogni-tion Using Range Imaging[C]//Proceedings of the 7th International Symposium onSignal Processing and Its Applications. Paris, France, 2003:201– 204.
74 C. Beumier, M. Acheroy. Face Verification from 3D and Grey Level Cues[J]. Pat-tern Recognition Letters, 2001, 12(22):1321– 1329.
75 X. Lu, A. Jain, D. Colbry. Deformation Analysis for 3D Face Match-ing[C]//Proceedings of IEEE Workshop on Applications of Computer Vision.Breckenridge, USA, 2005:99– 104.
76 F. Tsalakanidou, D. Tzocaras, M. Strintzis. Use of Depth and Colour Eigenfacesfor Face Recognition[J]. Pattern Recognition Letters, 2003, 9(24):1427– 1435.
77 F. Tsalakanidou, S. Malasiotis, M. Strintzis. Face Localization and AuthenticationUsing Color and Depth Images[J]. IEEE Transactions on Image Processing, 2005,2(14):152– 168.
78 K. Chang, K. Bowyer, P. Flynn. Face Recognition Using 2D and 3D FacialData[C]//Proceedings of Multimodal User Authentication Workshop. Santa Bar-bara, USA, 2003:25– 32.
79 T. Maurer, D. Guigonis, I. Maslov, et al. Performance of Geometrix Active TM 3DFace Recognition Engine on the FRGC Data[C]//Proceedings of IEEE Workshopon Face Recognition Grand Challenge Experiments. San Diego, USA, 2005:210–216.
80 A. Mian, M. Bennamoun, R. Owens. 2D & 3D Multimodel Hybrid Face Recogni-tion[C]//Proceedings of European Conference on Computer Vision. Graz, Austria,2006:344– 355.
81 A. Mian, M. Bennamoun, R. Owens. An Efficient Multimodal 2D-3D Hybrid Ap-proach to Automatic Face Recognition[J]. IEEE Transactions on Pattern Analysisand Machine Intelligence, 2007, 11(29):1927– 1943.
82 T. Papatheodorou, D. Reuckert. Evaluation of Automatic 4D Face RecognitionUsing Surface and Texture Regist Ration[C]//Proceedings of the 6th InternationalConference on Automated Face and Gesture Recognition. Seoul, Korea, 2004:321– 326.
83 M. van der Veen, T. Kevenaar, G.-J. Schrijen, et al. Face Biometrics with RenewableTemplates[C]//Proceedings of SPIE: Security, Steganography, and Watermarking ofMultimedia Contents VIII. San Jose, USA, 2006.
84 A. Cavoukian, A. Stoianov. Biometric Encryption : A Positive-sum TechnologyThat Achieves Strong Authentication, Security and Privacy[C]//Information andPrivacy Commissioner. Ontario, 2007.
85 E. Verbitskiy, P. Tuyls, D. Denteneer, et al. Reliable Biometric Authentication withPrivacy Protection[C]//Proceedings of 24th Benelux Symposium on InformationTheory. Mierlo, Australia, 2003.
86 P. Tuyls, J. Goseling. Capacity and Examples of Template Protecting Biomet-ric Authentication Systems[C]//Proceedings of Biometric authentication workshop(BioAW’04). Prague, Czech, 2004:158– 170.
87 S.-W. Sun, C.-S. Lu, P.-C. Chang. Biometric Template Protection: A Key-mixedTemplate Approach[C]//Digest of Technical Papers of IEEE International Confer-ence on Consumer Electronics (ICCE’07). Hiroshima, 2007:1– 2.
88 M. Mimura, S. Ishida, Y. Seto. Fingerprint Verification System on SmartCard[C]//Digest of Technical Papers of International Conference of ConsumerElectronics (ICCE’02). Auckland, New Zealand, 2002:182– 183.
89 N. Ratha, J. Connell, R. Bolle. Enhancing Security and Privacy in Biometrics-basedAuthentication Systems[J]. IBM Systems Journal, 2001, 3(40):614– 634.
90 N. Ratha, J. Connell, R. Bolle. Cancelable Biometrics: A Case Study inFingerprints[C]//Proceedings of International Conference on Pattern Recognition(ICPR’06). Hong Kong, China, 2006:370– 373.
91 G. Davida, Y. Frankel, B. Matt. On Enabling Secure Applications Through Off-line Biometric Identification[C]//Proceedings of IEEE Symposium on Security andPrivacy. Oakland, USA, 1998:148– 157.
92 T. Connie, A. Teoh, M. Goh, et al. Palmhashing: A Novel Approach for CancelableBiometrics[J]. Information Processing Letters, 2005, 93(2):614– 634.
93 A. Teoh, D. Ngo, A. Goh. Personalised Cryptographic Key Generation Based onFacehashing[J]. Computers and Security, 2004, 23(5):606– 614.
94 S. Tulyakov, F. Farooq, V. Govindaraju. Symmetric Hash Functions for FingerprintMinutiae[J]. Lecture Notes in Computer Science, 2005, 3687:30– 38.
95 A. Bodo. Method for Producing a Digital Signature with Aid of a Biometric Fea-ture[J]. German patent DE 42439081 A1, 1994.
96 C. Tomko, C. Soutar, G. Schmidt. Fingerprint Controlled Public Key CryptographicSystem[J]. U.S. Patent 5541994, 1996.
97 C. Tomko, C. Soutar, G. Schmidt. Biometric Controlled Key Generation[J]. U. S.Patent 5680460, 1997.
98 C. Tomko, A. Stoianov. Method and Apparatus for Securely Handling a PersonalIdentification Number Or Cryptographic Key Using Biometric Techniques[J]. U.S.Patent 5712912, 1998.
99 A. Juels, M. Wattenbeg. A Fuzzy Commitment Scheme[C]//Proceedings of ACMConference on Computer and Communications Security. New York, USA: ACMPress, 1999:28– 36.
100 A. Juels, M. Sudan. A Fuzzy Vault Scheme[C]//Proceedings of IEEE InternationalSymposium on Information Theory. Lausanne, Switzerland, 2002:408.
101 J. Linnartz, P. Tuyls. New Shiedling Functions to Enhance Privacy and Prevent Mis-use of Biometric Templates[C]//Proceedings of international conference on audio-and video-based biometric person authentication. Guildford, 2003:393– 402.
102 P. Tuyls, E. Verbitskiy, J. Goseling, et al. Privacy Protecting Biometric Authen-tication Systems: An Overview[C]//XII European signal processing conference.Austria, 2004:1397– 1400.
103 Y. Dodis, R. Ostrovsky, L. Reyzin, et al. Fuzzy Extrators: How to Generate StrongSecret Keys from Biometrics and Other Noisy Data[C]//Advances in cryptology -Eurocrypt’04. Interlaken, 2004:523– 540.
104 Y. Dodis, R. Ostrovsky, L. Reyzin, et al. Fuzzy Extrators: How to Generate StrongSecret Keys from Biometrics and Other Noisy Data(version 2)[J]. SLAM Journalon computing, 2008, 38(1).
105 X. Boyen. Reusable Cryptographic Fuzzy Extractors[C]//Proceedings of the 11thACM conference on Computer and communications security. Washington DC,USA, 2004:82– 91.
106 T. C. Clancy, N. Kiyavash, D. J. Lin. Secure Smartcard-based Fingerprint Au-thentication[C]//Proceedings of the 2003 ACM SIGMM workshop on Biometricsmethods and applications. Berkley, USA, 2003:45– 52.
107 U. Uludag, S. Pankanti, S. Prabhakar, et al. Biometric Cryptosystems: Issues andChallenges[J]. Proceesings of the IEEE, 2004, 92(6):948– 960.
108 A. Jain, A. Ross, S. Pankanti. Biometrics: A Tool for Information Security[J]. IEEEtransaction on information forensics and security, 2006, 1(2):125– 143.
109 U. Uludag, S. Pankanti, A. Jain. Fuzzy Vault for Fingerprints[C]//AVBPA2005: audio- and video- based biometric person authentication. New York, USA,2005:310– 319.
110 S. Yang, I. Verbauwhede. Secure Fuzzy Vault Based Fingerprint Verification Sys-tem[C]//Conference Record of the 38th Asilomar Conference on Signals, Systemsand Computers. Lexington, MA, USA, 2004:577– 581.
111 A. Nagar, S. Chaudhury. Biometrics Based Asymmetric Cryptosystem Design Us-ing Modified Fuzzy Vault Scheme[C]//Proceedings of International conference onpattern recognition (ICPR’06). Hong Kong, China, 2006:537– 540.
112 K. Nandakumar, A. K. Jain, S. Pankanti. Fingerprint-based Fuzzy Vault: Imple-mentation and Performance[J]. IEEE Transaction on Information Forensics andSecurity, 2007, 2(4):744– 757.
113 S. Lee, D. Moon, Y. Chung. Knowledge-based Intelligent Information and Engi-neering Systems[C]//7th International Conference, KES 2003. Oxford, 2003.
114 Q. Li, M. Guo, E.-C. Chang. Fuzzy Extractors for Asymmetric Biometric Represen-tation[C]//Proceedings of IEEE Computer Society Conference on Computer Visionand Pattern Recognition Workshops (CVPRW’08). Anchorage, USA, 2008:1– 6.
115 A. Kholmatov, B. Yanikoglu. Realization of Correlation Attack Against the FuzzyVault Scheme[C]//SPIE : Security, forensics, steganography, and watermarking ofmultimedia contents. San Jose, USA, 2008:68190O.1– 68190O.7.
116 P. Tuyls, A. M. Akkermans, T. Kevenaar, et al. Practical Biometric Authenti-cation with Template Protection[C]//Proceedings of 5th International ConferenceAudio- and Video-Based Biometric Person Authentication (AVBPA 2005). NewYork, USA, 2005:436– 446.
117 Z. zeng, P. A. Watters. A Novel Face Hashing Method with Feature Fusion for Bio-metric Cryptosystems[C]//Proceedings of Fourth European Conference UniversalMultiservice Networks (ECUMN’07). Toulouse, France, 2007:439– 444.
118 Y. Wang, K. N. Plataniotis. Face Based Biometric Authentication with Changeableand Privacy Preservable Templates[C]//Biometrics Symposium. Maryland, USA,2007:1– 6.
119 A. Jin, J. Kim. Fuzzyhash: A Secure Biometric Template Protection Tech-nique[C]//Frontiers in the Convergence of Bioscience and Information Technolo-gies(FBIT’07). Jeju Island, 2007:688– 694.
120 Y. Sutcu, Q. Li, N. Memon. Protecting Biometric Templates with Sketch: Theoryand Practice[J]. IEEE Transactions on Information Forensics and Securit, 2007,2(3):744– 757.
121 F. Hao, R. Anderson, J. Daugman. Combining Crypto with Biometrics Effec-tively[J]. IEEE Transactions on Computers, 2006, 55(9):1081– 1088.
122 C. Ercole, P. Campisi, A. Neri. Iris-based Authentication System with TemplateProtection and Renewability[C]//Proceedings of SPIE, the International Society forOptical Engineering. Florence, 2007:67410H.1– 67410H.8.
123 Y. J. Lee, K. R. Park, S. J. Lee, et al. A New Method for Generating an InvariantIris Private Key Based on Fuzzy Vault System[J]. IEEE Transactions on systems,man, and cybernetics-part B: cybernetics, 2008, 38(5).
124 Q. Li, X. Niu, S. Sun. A Novel Biometric Key Scheme[J]. Chinese Journal ofelectronics, 2005, 2(3).
125 F. Hao, C. W. Chan. Private Key Generation from On-line Handwritten Signa-tures[J]. Information management & computer security, 2002, 10(2):159– 164.
126 P. Campisi, E. Maiorana, M. G. Prats, et al. Adaptive and Distributed Cryptographyfor Signature Biometrics Protection[C]//Proceedings of SPIE: Security, Steganog-raphy, and Watermarking of Multimedia Contents IX. San Jose, USA, 2007.
127 M. R. Freire, J. Fierrez, J. Ortega-Garcia. Dynamic Signature Verification withTemplate Protection Using Helper Data[C]//Proceedings of IEEE International con-ference on Acoustics, speech, and signal processing (ICASSP 2008). Las Vegas,USA, 2008:1713– 1716.
128 E. Kelkboom, T. Kevenaar, A. Akkermans, et al. 3D Face: Biometric TemplateProtection[C]//Proceedings of 2nd International Conference on Biometrics. Seoul,Korea, 2007:566– 573.
129 X. Zhou. Template Protection and its Implementation in 3D Face Recognition Sys-tems[C]//SPIE : Biometric Technology for Human Identification IV. Orlando, USA,2007.
130 T. Franssen, X. Zhou. Fuzzy Vault for 3d Face Recognition System[C]//Proceedingsof International conference on Intelligent information hiding multimedia signal pro-cessing. Harbin, China, 2008:1069– 1074.
131 X. Lu, A. K. Jain. Automatic Feature Extraction for Multiview 3D Face Recog-nition[C]//Proceedings of International conference on automatic face and Gesturerecognition. UK, 2006:1069– 1074.
132 X. Zhou, H. Seibert, C. Busch, et al. A 3D Face Recognition Algorithm Us-ing Histogram-based Features[C]//Eurographics 2008 Workshop on 3D Object Re-trieval. Crete, Greece, 2008.
133 G. HETZEL, B. LEIBE, P. LEVI, et al. 3D Object Recognition from Range Im-ages Using Local Feature Histograms[C]//Proceedings of IEEE International Con-ference on Computer Vision and Pattern Recognition (CVPR’01). Hawaii, USA,2001:394– 399.
134 P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman. Eigenfaces Vs. Fisherfaces:Recognition Using Class Specific Linear Projection[J]. IEEE TRANSACTIONON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19(7).
135 D. Swets, J. Weng. Using Discriminant Eigenfeatures for Image Retrieval[J]. IEEETransaction on Pattern ANALYSIS AND MACHINE INTELLIGENCE, 1996,18(8):831– 836.
136 J. Yang, A. F. Frangi, J. yu Yang, et al. KPCA Plus LDA: A Complete KernelFisher Discriminant Framework for Feature Extraction and Recognition[J]. IEEETransactions on pattern analysis and machine intelligence, 2005, 27(2).
137 J. Yang, J. yu Yang, A. F. Frangi. Combined Fisherfaces Framework[J]. Image andVision Computing, 2003, 27(2):1037– 1044.
138李琼.生物识别模糊性与生物密钥算法研究[D].哈尔滨:哈尔滨工业大学,2005.
2 K. Jain, R. Bolle, S. Pankanti. Personal Identification Innetworked Society[M].Norwell: Kluwer Academic, 1999:50– 70.
3 M. Jafar, A. Hassanien. An Iris Recognition System to Enhancee-security Environ-ment Based on Wavelet Theory[J]. Advanced Modeling and Optimization, 2003,2(5):93– 104.
4 G. Gordon. Face Recognition Based on Depth and Curvature Fea-tures[C]//Proceedings of IEEE Computer Society Conference on Computer Visionand Pattern Recognition (CVPR’92). Champaign, USA, 1992:808– 810.
5 C. Hesher, A. Srivastava, G. Erlebacher. A Novel Technique for Face Recogni-tion Using Range Images[C]//Proceedings of International Symposium on SignalProcessing and Its Applications. Paris, France, 2003:201– 204.
6 G. Medioni, R. Waupotitsch. Face Recognition and Modeling in 3D[C]//IEEE In-ternational Workshop on Analysis and modeling of Faces and Gestures (AMFG2003). Nice, France, 2003:232– 223.
7 P. Phillips, P. Flynn, T. Scruggs, et al. Overview of the Face Recognition GrandChallenge[C]//Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition. San Diego, USA, 2005:947– 951.
8 C. Soutar, D. Roberge, A. Stoianov, et al. Method for Secure Key ManagementUsing a Biometric[J]. U.S. Patent, 2001, (6219794).
9 T. Matsumoto, H. Matsumoto, K. Yamada, et al. Impact of Artificial Gummy Fin-gers on Fingerprint Systems[C]//Proceedings of SPIE conference on Optical Secu-rity and Counterfeit Deterrence Techniques IV. San Jose, USA, 2002.
10王跃明,潘纲,吴朝晖.三维人脸识别综述[J].计算机辅助设计与图形学学报, 2008, 20(7):819– 829.
11刘向东.全自动三维人脸识别框架[D].吉林:吉林大学, 2007:1– 5.
12 K. Chang, K. Bowyer, P. Flynn. Face Recognition Using 2D and 3D Fa-cial Data[C]//Multimodal User Authentication WorkShop. Santa Barbara, USA,2003:25– 32.
13 S. Malassiotis, M. Strintzis. Real Time Head Tracking and 3D Pose Estimationfrom Range Data[C]//Processings of International Conference on Image (ICIP’03).Barcelona, Spain, 2003.
14 D. A. Field. Laplacian Smoothing and Delaunay Triangulations[J]. Communica-tions in Applied Numerical Methods, 1988, 4(6):709– 712.
15 G. Taubin. A Signal Processing Approach to Fair Surface Design[C]//Proceedingsof the 22nd annual conference on Computer graphics and interactive techniques.New York, USA, 1995:351– 358.
16 G. Taubin, T. Zhang, G. Golub. Optimal Surface Smoothing as FilterDesign[C]//Proceedings of the 4th European Conference on Computer Vi-sion(ECCV’96). Cambridge, UK, 1996:283– 292.
17中煜湘,邹北骥,彭永进.三基于面分块的三维人脸模型的边界表示法[J].湖南大学学报, 2001, 5(28):112– 117.
18 T. Fujiwara, T. Nishihara, M. Tominaga, et al. On the Detection of Feature Pointsof 3D Facial Image and its Application to 3D Facial Caricature[C]//Second Inter-national Conference on 3D Imaging and Modeling (3DIM’99). Ottawa, Canada,1999:4– 9.
19 C. Dorai, A. Jain. Cosmos: A Representation Scheme for 3D Free-form Ob-jects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(10):1115– 1130.
20 C. Chua, R. Jarvis. Point Signayure: A New Representation for 3D Object Recog-nition[J]. International Journal on Computer Vision, 1997, 1(25):63– 85.
21 Y. Wang, C.-S. Chua, Y.-K. Ho. Facial Feature Detection and Face Recognitionfrom 2D and 3D Images[J]. Pattern Recognition Letters, 2002, 10(23):1191– 1202.
22 X. Lu, D. Colbry, A. Jain. Three Dimensional Model Based Face Recogni-tion[C]//Proceedings of International Conference Pattern Recognition (ICPR’04).Cambridge, USA, 2004:362– 366.
23 L. Wiskott, J.-M. Fellous, et al. Face Recognition by Elastic Bunch Graph Match-ing[J]. IEEE Transactions on pattern analysis and maching intelligence, 1997,7(19):775– 779.
24 A. B. Moreno, A. Sanchez, J. F. Velez, et al. Face Recognition Using 3d Surface-extracted Descriptors[C]//Proceedings of Irish maching vision and image process-ing(IMVIP’03). Maynooth, Ireland, 2003:50– 57.
25 D. Huber, M. Hebert. Fully Automatic Registration of Multiple 3D Data Sets UsingGaussian Fields[J]. Image and Vision Computing, 2003, 21(7):637– 650.
26 B. K. P. Horn. Closed Form Solution of Absolute Orientation Using Unit Quater-nions[J]. Journal of the Optical Society of America, 1987, 4(4):629– 642.
27 P. Besl, N. McKay. A Method for Registration of 3D Shapes[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1992, 2(14):239– 256.
28 F. Mokhtarian. A Theory of Multiscale, Torsion Based Shape Representation[J].Computer vision and image understanding, 1997, 68(1):1– 17.
29 J. Feldmar, N. Ayache, F. Betting. 3D-2D Projective Registration of Free-form Curves and Surfaces[J]. Computer vision and image understanding, 1997,65(3):403–424.
30 K. S. Arun, T. S. Huang, S. Blostein. Least Square Fitting of Two 3D Point Sets[J].IEEE Transactions on Pattern Analysis and Maching Intelligence, 1987, 9(5):698–700.
31严庆光,李明哲,李东成.多点成形件检测中三维数据配准方法的研究[J].中国机械工程, 2003, 14(19):1648– 1651.
32彭育辉,高诚辉,何炳蔚.基于遗传算法和四元数法的曲面线扫描数据的配准[J].福州大学学报(自然科学版), 2008, 36(4):541– 546.
33 O. D. Faugeras, M. Hebert. The Representation, Recognition, and Locating of 3DObjects[J]. International Journal of Robotics Research, 1986, 5(3):27– 52.
34 O. D. Faugeras, M. Hebert. Using the Gaussian Image to Find the Orienttion of anObject[J]. International Journal of Robotics Research, 1983, 3(4):89– 125.
35 B. Horn. Extended Gaussian Images[J]. Proceedings of the IEEE, 1984,72(12):1656– 1678.
36 S. Rusinkiewicz, M. Levoy. Efficient Variants of the ICP Algo-rithm[C]//Proceedings of the International conference on 3D digital imagingand modeling(3DIM’01). Quebec, Canada, 2001:145– 152.
37 S. Rusinkiewicz, O. Hall-Holt, M. Levoy. Real-time 3D Model Acquisition[J].ACM Transactions on Graphics (TOG), 2002, 21(3):438– 446.
38 K. Chang, K. Bowyer, P. Flynn. Effects on Facial Expression in 3D Face Recogni-tion[C]//Proceedings of SPIE Conference Biometric Technology for Human Iden-tification. Orlando, USA, 2005:132– 143.
39 Y. Chen, G. Medioni. Object Modeling by Registration of Multiple Range Im-ages[J]. Image and vision computing, 1992, 10(3):145– 155.
40 T. Z. Jochen, J. Schmidt, H. Niemann. A Refined Icp Algorithm for Robust 3DCorrespondence Estimation[C]//Proceedings of International Conference on ImageProcessing (ICIP’03). Barcelona, Spain, 2003.
41 J. Cook, V. Ch, S. Sridharan, et al. Face Recognition from 3D Data Using IterativeClosest Point Algorithm and Gaussian Mixture Models[C]//Proceedings of the 2ndInternational Symposium on 3D Data Processing , Visualization and Transmission.Thessaloniki, Greece, 2004:502– 509.
42 X. Lu, A. Jain, D. Colbry. Matching 2.5D Face Scans to 3D Models[J]. IEEETransactions on Pattern Analysis and Machine Intelligence, 2006, 1(28):31– 43.
43 Y. Wang, G. Pan, Z. Wu, et al. Exploring Facial Expression Effects in 3D FaceRecognition Using Partial ICP[C]//Proceedings of the 7th Asian Conference onComputer Vision. Hyderabad, India, 2006:581– 590.
44 K. Chang, K. Bowyer, P. Flynn. Adaptive Rigid Multi-region Selection for Han-dling Expression Variation in 3D Face Recognition[C]//IEEE workshop on facerocognition grand challenge experiments. SanDiego, USA, 2005:157– 160.
45 B. Achermann, H. Bunke. Classifying Range Images of Human Faces with Haus-dorff Distance[C]//International Conference on Pattern Recognition (ICPR 2000).Barcelona, Spain, 2000:809– 813.
46 Y. Lee, J. Shim. Curvature-based Human Face Recognition Using Depth-weightedHausdorff Distance[C]//International Conference on Image Processing(ICIP’04).Singapore, 2004:1429– 1432.
47 T. Russ, K. Koch, C. Little. 3D Facial Recognition: A Quantitative Analy-sis[C]//38th Annual 2004 International Carnahan Conference on Security Technol-ogy. Albuquerque, USA, 2004:338– 344.
48 T. Russ, M. Koch, C. Little. A 2D Range Hausdorff Approach for 3D Face Recogni-tion[C]//IEEE Workshop on Face Recognition Grand Challenge Experiments. SanDiego, USA, 2005:169– 175.
49 T. K. Kim, S. C. Kee, S. R. Kim. Real-time Normalization and Feature Extractionof 3D Face Data Using Curvature Characteristics[C]//IEEE International workshopon robot and human interactive comminication. Paris, France, 2001:74– 79.
50 P. Yuen, F. Mokhtarian, N. Khalili, et al. Curvature and Torison Feature Extractionfrom Free-form 3D Meshs at Multiple Scales[J]. IEEE Proceedings vision, imageand signal processing, 2000, 147(5):454– 462.
51 M. Nomura, N. Hamada. Feature Edge Extraction from 3D Triangular MeshesUsing a Thinning Algorithm[J]. Institute of Electronics, Information and Commu-nication Engineers, 2001, 100(443):107– 118.
52孙海鹏,毕笃彦,陶杰.曲率及几何约束的三维人脸标志点定位[J].微计算机信息, 2009, 25(7):264– 266.
53 K. Chang, K. Bowyer, P. Flynn. Adaptive Rigid Multi-region Selection forHandling Expression Variation in 3D Face Recognition[C]//Proceedings of IEEEWorkshop on Face Recognition Grand Challenge Experiments. San Diego, USA,2005:157– 163.
54 Y. Lee, J. Shim. Curvature-based Human Face Recognition Using Depth WeightedHausdorff Distance[C]//Proceedings of International Conference on Image Process-ing. Singapore, 2004:1492– 1432.
55郭哲,张艳宁,林增刚.基于曲率信息的三维人脸面部特征提取[J].计算机工程与应用, 2007, 43(24):239– 245.
56 A. B. Moreno, A. Sanchez, J. Velez. Face Recognition Using 3D Surface-extractedDescriptors[C]//Proceedings of Irish Machine Vision and Image Processing. Ire-land, 2003:50– 57.
57 J. Lee, E. Milios. Matching Range Images of Human Faces[C]//Proceedings ofInternational Conference on Computer Vision. Osaka, Japan, 1990:722– 726.
58 A. Makadia, A. Patterson, K.Daniilidis. Fully Automatic Registration of 3d PointClouds[C]//IEEE Computer Society Conference on Computer Vision and PatternRecognition. New York, USA, 2006:1297– 1304.
59 H. T. Tanaka, M. Ikeda. Curvature-based Face Surface Recognition Using SphericalCorrelation-principal Directions for Curved Object Recognition[C]//Proceedings ofthe 3rd International Conference on Automated Face and Gesture Recognition. Vi-enna, Austria, 1998:372– 377.
60 G. Pan, Y. Wu, Z. Wu. Investigating Profile Extraction from Range Data for 3DRecognition[C]//Proceedings of IEEE International conference on multimedia &expo (ICME’04). Taipei, 2004:6– 9.
61 T. Nagamine, T. Uemura, I. Masuda. 3D Facial Image Analysis for Human Identifi-cation[C]//Proceedings of International Conference on Pattern Recognition. Hague,Netherlands, 1992:324– 327.
62 G. Pan, Z. Wu. 3D Face Recognition from Range Data[J]. International Journal ofImage and Graphics, 2005, 3(5):573– 593.
63 C. Chua, F. H. Y. Ho. 3D Human Face Recognition Using Point Signa-ture[C]//Proceedings of International Conference on Automatic Face and GestureRecognition. Grenoble, France, 2000:233– 238.
64 D. Colbry, G. Stockman. Canonical Face Depth Map : A Robust 3D Representationfor Face Verification[C]//Proceedings of IEEE Computer Society Conference onComputer Vision and Pattern Recognition. Minneapolis, USA, 2007:102– 109.
65 A. Bronstein, M. Bronstein, R. Kimmel. Three-dimensional Face Recognition[J].International Journal of Computer Vision, 2005, 1(64):5– 30.
66 A. Bronstein, M. Bronstein, R. Kimmel. Robust Expression Invariant Face Recog-nition from Partially Missing Data[C]//Proceedings of European Conference onComputer Vision. Graz, Austria, 2006:396– 408.
67 Y. Wang, G. Pan, Z. Wu. 3D Face Recognition in the Presence of Expression :A Guidance-based Const Raint Deformation Approach[C]//Proceedings of IEEEComputer Society Conference on Computer Vision and Pattern Recognition. Min-neapolis, USA, 2007:201– 207.
68 C. Xu, Y. Wang, T. Tan, et al. Automatic 3D Face Recognition Combining GlobalGeometric Features with Local Shape Variation Information[C]//Proceedings ofIEEE International Conference on Automatic Face and Gesture Recognition. Seoul,Korea, 2004:308– 313.
69 C. Zhong, Z. Sun, T. Tan. Robust 3D Face Recognition Using Learned VisualCodebook[C]//Proceedings of IEEE Computer Society Conference on ComputerVision and Pattern Recognition. Minneapolis, USA, 2007:12– 17.
70 O. Chapelle, P. Haffner, V. Vapnik. Support Vector Machines for Histogram-basedImage Classification[J]. IEEE Transactions on neural networks, 1999, 10(5):1055– 1064.
71 T. Iwasa, T. Shima, M. Sai, et al. 3D Eigenfaces for Face Modeling[C]//Proceedingsof Asian Conference on Computer Vision, ACCV’02. Australia, 2002:308– 313.
72 J. Lei, C. Lu. 3D Eigenfaces for Face Modeling[C]//Proceedings of ACIS Inter-national Conference on Software Engineering, Artificial Intelligence, Networking,and Parallel/Distributed Computing (SNPD’07). Qingdao, China, 2007:501– 506.
73 C. Hesher, A. Srivastava, G. Erlebacher. A Novel Technique for Face Recogni-tion Using Range Imaging[C]//Proceedings of the 7th International Symposium onSignal Processing and Its Applications. Paris, France, 2003:201– 204.
74 C. Beumier, M. Acheroy. Face Verification from 3D and Grey Level Cues[J]. Pat-tern Recognition Letters, 2001, 12(22):1321– 1329.
75 X. Lu, A. Jain, D. Colbry. Deformation Analysis for 3D Face Match-ing[C]//Proceedings of IEEE Workshop on Applications of Computer Vision.Breckenridge, USA, 2005:99– 104.
76 F. Tsalakanidou, D. Tzocaras, M. Strintzis. Use of Depth and Colour Eigenfacesfor Face Recognition[J]. Pattern Recognition Letters, 2003, 9(24):1427– 1435.
77 F. Tsalakanidou, S. Malasiotis, M. Strintzis. Face Localization and AuthenticationUsing Color and Depth Images[J]. IEEE Transactions on Image Processing, 2005,2(14):152– 168.
78 K. Chang, K. Bowyer, P. Flynn. Face Recognition Using 2D and 3D FacialData[C]//Proceedings of Multimodal User Authentication Workshop. Santa Bar-bara, USA, 2003:25– 32.
79 T. Maurer, D. Guigonis, I. Maslov, et al. Performance of Geometrix Active TM 3DFace Recognition Engine on the FRGC Data[C]//Proceedings of IEEE Workshopon Face Recognition Grand Challenge Experiments. San Diego, USA, 2005:210–216.
80 A. Mian, M. Bennamoun, R. Owens. 2D & 3D Multimodel Hybrid Face Recogni-tion[C]//Proceedings of European Conference on Computer Vision. Graz, Austria,2006:344– 355.
81 A. Mian, M. Bennamoun, R. Owens. An Efficient Multimodal 2D-3D Hybrid Ap-proach to Automatic Face Recognition[J]. IEEE Transactions on Pattern Analysisand Machine Intelligence, 2007, 11(29):1927– 1943.
82 T. Papatheodorou, D. Reuckert. Evaluation of Automatic 4D Face RecognitionUsing Surface and Texture Regist Ration[C]//Proceedings of the 6th InternationalConference on Automated Face and Gesture Recognition. Seoul, Korea, 2004:321– 326.
83 M. van der Veen, T. Kevenaar, G.-J. Schrijen, et al. Face Biometrics with RenewableTemplates[C]//Proceedings of SPIE: Security, Steganography, and Watermarking ofMultimedia Contents VIII. San Jose, USA, 2006.
84 A. Cavoukian, A. Stoianov. Biometric Encryption : A Positive-sum TechnologyThat Achieves Strong Authentication, Security and Privacy[C]//Information andPrivacy Commissioner. Ontario, 2007.
85 E. Verbitskiy, P. Tuyls, D. Denteneer, et al. Reliable Biometric Authentication withPrivacy Protection[C]//Proceedings of 24th Benelux Symposium on InformationTheory. Mierlo, Australia, 2003.
86 P. Tuyls, J. Goseling. Capacity and Examples of Template Protecting Biomet-ric Authentication Systems[C]//Proceedings of Biometric authentication workshop(BioAW’04). Prague, Czech, 2004:158– 170.
87 S.-W. Sun, C.-S. Lu, P.-C. Chang. Biometric Template Protection: A Key-mixedTemplate Approach[C]//Digest of Technical Papers of IEEE International Confer-ence on Consumer Electronics (ICCE’07). Hiroshima, 2007:1– 2.
88 M. Mimura, S. Ishida, Y. Seto. Fingerprint Verification System on SmartCard[C]//Digest of Technical Papers of International Conference of ConsumerElectronics (ICCE’02). Auckland, New Zealand, 2002:182– 183.
89 N. Ratha, J. Connell, R. Bolle. Enhancing Security and Privacy in Biometrics-basedAuthentication Systems[J]. IBM Systems Journal, 2001, 3(40):614– 634.
90 N. Ratha, J. Connell, R. Bolle. Cancelable Biometrics: A Case Study inFingerprints[C]//Proceedings of International Conference on Pattern Recognition(ICPR’06). Hong Kong, China, 2006:370– 373.
91 G. Davida, Y. Frankel, B. Matt. On Enabling Secure Applications Through Off-line Biometric Identification[C]//Proceedings of IEEE Symposium on Security andPrivacy. Oakland, USA, 1998:148– 157.
92 T. Connie, A. Teoh, M. Goh, et al. Palmhashing: A Novel Approach for CancelableBiometrics[J]. Information Processing Letters, 2005, 93(2):614– 634.
93 A. Teoh, D. Ngo, A. Goh. Personalised Cryptographic Key Generation Based onFacehashing[J]. Computers and Security, 2004, 23(5):606– 614.
94 S. Tulyakov, F. Farooq, V. Govindaraju. Symmetric Hash Functions for FingerprintMinutiae[J]. Lecture Notes in Computer Science, 2005, 3687:30– 38.
95 A. Bodo. Method for Producing a Digital Signature with Aid of a Biometric Fea-ture[J]. German patent DE 42439081 A1, 1994.
96 C. Tomko, C. Soutar, G. Schmidt. Fingerprint Controlled Public Key CryptographicSystem[J]. U.S. Patent 5541994, 1996.
97 C. Tomko, C. Soutar, G. Schmidt. Biometric Controlled Key Generation[J]. U. S.Patent 5680460, 1997.
98 C. Tomko, A. Stoianov. Method and Apparatus for Securely Handling a PersonalIdentification Number Or Cryptographic Key Using Biometric Techniques[J]. U.S.Patent 5712912, 1998.
99 A. Juels, M. Wattenbeg. A Fuzzy Commitment Scheme[C]//Proceedings of ACMConference on Computer and Communications Security. New York, USA: ACMPress, 1999:28– 36.
100 A. Juels, M. Sudan. A Fuzzy Vault Scheme[C]//Proceedings of IEEE InternationalSymposium on Information Theory. Lausanne, Switzerland, 2002:408.
101 J. Linnartz, P. Tuyls. New Shiedling Functions to Enhance Privacy and Prevent Mis-use of Biometric Templates[C]//Proceedings of international conference on audio-and video-based biometric person authentication. Guildford, 2003:393– 402.
102 P. Tuyls, E. Verbitskiy, J. Goseling, et al. Privacy Protecting Biometric Authen-tication Systems: An Overview[C]//XII European signal processing conference.Austria, 2004:1397– 1400.
103 Y. Dodis, R. Ostrovsky, L. Reyzin, et al. Fuzzy Extrators: How to Generate StrongSecret Keys from Biometrics and Other Noisy Data[C]//Advances in cryptology -Eurocrypt’04. Interlaken, 2004:523– 540.
104 Y. Dodis, R. Ostrovsky, L. Reyzin, et al. Fuzzy Extrators: How to Generate StrongSecret Keys from Biometrics and Other Noisy Data(version 2)[J]. SLAM Journalon computing, 2008, 38(1).
105 X. Boyen. Reusable Cryptographic Fuzzy Extractors[C]//Proceedings of the 11thACM conference on Computer and communications security. Washington DC,USA, 2004:82– 91.
106 T. C. Clancy, N. Kiyavash, D. J. Lin. Secure Smartcard-based Fingerprint Au-thentication[C]//Proceedings of the 2003 ACM SIGMM workshop on Biometricsmethods and applications. Berkley, USA, 2003:45– 52.
107 U. Uludag, S. Pankanti, S. Prabhakar, et al. Biometric Cryptosystems: Issues andChallenges[J]. Proceesings of the IEEE, 2004, 92(6):948– 960.
108 A. Jain, A. Ross, S. Pankanti. Biometrics: A Tool for Information Security[J]. IEEEtransaction on information forensics and security, 2006, 1(2):125– 143.
109 U. Uludag, S. Pankanti, A. Jain. Fuzzy Vault for Fingerprints[C]//AVBPA2005: audio- and video- based biometric person authentication. New York, USA,2005:310– 319.
110 S. Yang, I. Verbauwhede. Secure Fuzzy Vault Based Fingerprint Verification Sys-tem[C]//Conference Record of the 38th Asilomar Conference on Signals, Systemsand Computers. Lexington, MA, USA, 2004:577– 581.
111 A. Nagar, S. Chaudhury. Biometrics Based Asymmetric Cryptosystem Design Us-ing Modified Fuzzy Vault Scheme[C]//Proceedings of International conference onpattern recognition (ICPR’06). Hong Kong, China, 2006:537– 540.
112 K. Nandakumar, A. K. Jain, S. Pankanti. Fingerprint-based Fuzzy Vault: Imple-mentation and Performance[J]. IEEE Transaction on Information Forensics andSecurity, 2007, 2(4):744– 757.
113 S. Lee, D. Moon, Y. Chung. Knowledge-based Intelligent Information and Engi-neering Systems[C]//7th International Conference, KES 2003. Oxford, 2003.
114 Q. Li, M. Guo, E.-C. Chang. Fuzzy Extractors for Asymmetric Biometric Represen-tation[C]//Proceedings of IEEE Computer Society Conference on Computer Visionand Pattern Recognition Workshops (CVPRW’08). Anchorage, USA, 2008:1– 6.
115 A. Kholmatov, B. Yanikoglu. Realization of Correlation Attack Against the FuzzyVault Scheme[C]//SPIE : Security, forensics, steganography, and watermarking ofmultimedia contents. San Jose, USA, 2008:68190O.1– 68190O.7.
116 P. Tuyls, A. M. Akkermans, T. Kevenaar, et al. Practical Biometric Authenti-cation with Template Protection[C]//Proceedings of 5th International ConferenceAudio- and Video-Based Biometric Person Authentication (AVBPA 2005). NewYork, USA, 2005:436– 446.
117 Z. zeng, P. A. Watters. A Novel Face Hashing Method with Feature Fusion for Bio-metric Cryptosystems[C]//Proceedings of Fourth European Conference UniversalMultiservice Networks (ECUMN’07). Toulouse, France, 2007:439– 444.
118 Y. Wang, K. N. Plataniotis. Face Based Biometric Authentication with Changeableand Privacy Preservable Templates[C]//Biometrics Symposium. Maryland, USA,2007:1– 6.
119 A. Jin, J. Kim. Fuzzyhash: A Secure Biometric Template Protection Tech-nique[C]//Frontiers in the Convergence of Bioscience and Information Technolo-gies(FBIT’07). Jeju Island, 2007:688– 694.
120 Y. Sutcu, Q. Li, N. Memon. Protecting Biometric Templates with Sketch: Theoryand Practice[J]. IEEE Transactions on Information Forensics and Securit, 2007,2(3):744– 757.
121 F. Hao, R. Anderson, J. Daugman. Combining Crypto with Biometrics Effec-tively[J]. IEEE Transactions on Computers, 2006, 55(9):1081– 1088.
122 C. Ercole, P. Campisi, A. Neri. Iris-based Authentication System with TemplateProtection and Renewability[C]//Proceedings of SPIE, the International Society forOptical Engineering. Florence, 2007:67410H.1– 67410H.8.
123 Y. J. Lee, K. R. Park, S. J. Lee, et al. A New Method for Generating an InvariantIris Private Key Based on Fuzzy Vault System[J]. IEEE Transactions on systems,man, and cybernetics-part B: cybernetics, 2008, 38(5).
124 Q. Li, X. Niu, S. Sun. A Novel Biometric Key Scheme[J]. Chinese Journal ofelectronics, 2005, 2(3).
125 F. Hao, C. W. Chan. Private Key Generation from On-line Handwritten Signa-tures[J]. Information management & computer security, 2002, 10(2):159– 164.
126 P. Campisi, E. Maiorana, M. G. Prats, et al. Adaptive and Distributed Cryptographyfor Signature Biometrics Protection[C]//Proceedings of SPIE: Security, Steganog-raphy, and Watermarking of Multimedia Contents IX. San Jose, USA, 2007.
127 M. R. Freire, J. Fierrez, J. Ortega-Garcia. Dynamic Signature Verification withTemplate Protection Using Helper Data[C]//Proceedings of IEEE International con-ference on Acoustics, speech, and signal processing (ICASSP 2008). Las Vegas,USA, 2008:1713– 1716.
128 E. Kelkboom, T. Kevenaar, A. Akkermans, et al. 3D Face: Biometric TemplateProtection[C]//Proceedings of 2nd International Conference on Biometrics. Seoul,Korea, 2007:566– 573.
129 X. Zhou. Template Protection and its Implementation in 3D Face Recognition Sys-tems[C]//SPIE : Biometric Technology for Human Identification IV. Orlando, USA,2007.
130 T. Franssen, X. Zhou. Fuzzy Vault for 3d Face Recognition System[C]//Proceedingsof International conference on Intelligent information hiding multimedia signal pro-cessing. Harbin, China, 2008:1069– 1074.
131 X. Lu, A. K. Jain. Automatic Feature Extraction for Multiview 3D Face Recog-nition[C]//Proceedings of International conference on automatic face and Gesturerecognition. UK, 2006:1069– 1074.
132 X. Zhou, H. Seibert, C. Busch, et al. A 3D Face Recognition Algorithm Us-ing Histogram-based Features[C]//Eurographics 2008 Workshop on 3D Object Re-trieval. Crete, Greece, 2008.
133 G. HETZEL, B. LEIBE, P. LEVI, et al. 3D Object Recognition from Range Im-ages Using Local Feature Histograms[C]//Proceedings of IEEE International Con-ference on Computer Vision and Pattern Recognition (CVPR’01). Hawaii, USA,2001:394– 399.
134 P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman. Eigenfaces Vs. Fisherfaces:Recognition Using Class Specific Linear Projection[J]. IEEE TRANSACTIONON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19(7).
135 D. Swets, J. Weng. Using Discriminant Eigenfeatures for Image Retrieval[J]. IEEETransaction on Pattern ANALYSIS AND MACHINE INTELLIGENCE, 1996,18(8):831– 836.
136 J. Yang, A. F. Frangi, J. yu Yang, et al. KPCA Plus LDA: A Complete KernelFisher Discriminant Framework for Feature Extraction and Recognition[J]. IEEETransactions on pattern analysis and machine intelligence, 2005, 27(2).
137 J. Yang, J. yu Yang, A. F. Frangi. Combined Fisherfaces Framework[J]. Image andVision Computing, 2003, 27(2):1037– 1044.
138李琼.生物识别模糊性与生物密钥算法研究[D].哈尔滨:哈尔滨工业大学,2005.