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Identity Management in Face Recognition Systems

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Book cover Biometrics and Identity Management (BioID 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5372))

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Abstract

Face recognition is one of the most challenging biometric modalities for personal identification. This is due to a number of factors, including the complexity and variability of the signal captured by a face device. Several issues incur in the management of a face template as user’s identity. Data dimensionality reduction, compactness of the representation, uniqueness of the template and ageing effects, are just but a few of the issues to be addressed. In this paper we present the current state of the art in face recognition technology and how this related to the proper management of a user’s identity. Some real cases are presented and some conclusions are drawn.

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References

  1. Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Transactions Pattern Analysis and Machine Intelligence 12(1), 103–108 (1990)

    Article  Google Scholar 

  2. Turk, M., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  3. Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1999)

    Article  Google Scholar 

  4. Chengjun, L.: Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)

    Article  Google Scholar 

  5. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  MATH  Google Scholar 

  6. Li, S.Z., Hou, X.W., Zhang, H.J.: Learning spatially localized, parts-based representation. In: Proceedings CVPR, pp. 207–212 (2001)

    Google Scholar 

  7. Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)

    Article  MATH  Google Scholar 

  8. Juwei, L., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using lda-based algorithms. IEEE Transactions on Neural Networks 14(1), 195–200 (2003)

    Article  Google Scholar 

  9. Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12, 2385–2404 (2000)

    Article  Google Scholar 

  10. Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. Neural Networks 12(2), 181–201 (2001)

    Article  Google Scholar 

  11. Juwei, L., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Transactions on Neural Networks 14(1), 117–126 (2003)

    Article  Google Scholar 

  12. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.-R.: Fisher Discriminant Analysis with Kernels. In: Proceedings IEEE Int’l Workshop Neural Networks for Signal Processing IX, pp. 41–48 (August 1999)

    Google Scholar 

  13. Mika, S., Ratsch, G., Scholkopf, B., Smola, A., Weston, J., Muller, K.-R.: Invariant Feature Extraction and Classification in Kernel Spaces. In: Advances in Neural Information Processing Systems, vol. 12. MIT Press, Cambridge (1999)

    Google Scholar 

  14. Jain, A.K., Chandrasekaran, B.: Dimensionality and sample size considerations in pattern recognition practice. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of Statistics. vol. 2, pp. 835–855. North-Holland, Amsterdam (1987)

    Google Scholar 

  15. Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 252–264 (1991)

    Article  Google Scholar 

  16. Martinez, A., Kak, A.: PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)

    Article  Google Scholar 

  17. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)

    Article  Google Scholar 

  18. Chengjun, L., Wechsler, H.: Independent component analysis of Gabor features for face recognition. IEEE Transactions on Neural Networks 14(4), 919–928 (2003)

    Article  Google Scholar 

  19. Bach, F., Jordan, M.: Kernel Independent Component Analysis. Journal of Machine Learning Research 3, 1–48 (2002)

    MathSciNet  MATH  Google Scholar 

  20. Lades, M., et al.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. on Computers 42(3), 300–311 (1993)

    Article  Google Scholar 

  21. Zhang, J., Yan, Y., Lades, M.: Face recognition: eigenface, elastic matching, and neural nets. Proceedings of the IEEE 85(9), 1423–1435 (1997)

    Article  Google Scholar 

  22. Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.v.d.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)

    Article  Google Scholar 

  23. Wiskott, L.: Phantom faces for face analysis. Pattern Recognition 30(6), 837–846 (1997)

    Article  Google Scholar 

  24. Wurtz, R.P.: Object recognition robust under translations, deformations, and changes in background. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 769–775 (1997)

    Article  Google Scholar 

  25. Duc, B., Fischer, S., Bigun, J.: Face authentication with Gabor information on deformable graphs. IEEE Transactions on Image Processing 8(4), 504–516 (1999)

    Article  Google Scholar 

  26. Kotropoulos, C., Tefas, A., Pitas, I.: Frontal face authentication using discriminating grids with morphological feature vectors. IEEE Transactions on Multimedia 2(1), 14–26 (2000)

    Article  MATH  Google Scholar 

  27. Kotropoulos, C., Tefas, A., Pitas, I.: Morphological elastic graph matching applied to frontal face authentication under well-controlled and real conditions. Pattern Recognition 33(12), 31–43 (2000)

    Article  MATH  Google Scholar 

  28. Kotropoulos, C., Tefas, A., Pitas, I.: Frontal face authentication using morphological elastic graph matching. IEEE Transactions on Image Processing 9(4), 555–560 (2000)

    Article  MATH  Google Scholar 

  29. Jackway, P.T., Deriche, M.: Scale-space properties of the multiscale morphological dilation-erosion. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(1), 38–51 (1996)

    Article  Google Scholar 

  30. Tefas, A., Kotropoulos, C., Pitas, I.: Face verification using elastic graph matching based on morphological signal decomposition. Signal Processing 82(6), 833–851 (2002)

    Article  MATH  Google Scholar 

  31. Kruger, N.: An algorithm for the learning of weights in discrimination functions using A priori constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 764–768 (1997)

    Article  Google Scholar 

  32. Tefas, A., Kotropoulos, C., Pitas, I.: Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(7), 735–746 (2001)

    Article  Google Scholar 

  33. Wang, H., Li, S.Z., Wang, Y., Zhang, W.: Illumination Modeling and Normalization for Face Recognition. In: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, Nice, France (2003)

    Google Scholar 

  34. Chang, K.I., Bowyer, K.W., Flynn, P.J.: Face Recognition Using 2D and 3D Facial Data. In: Proceedings Workshop in Multimodal User Authentication, Santa Barbara, California, pp. 25–32 (December 2003)

    Google Scholar 

  35. Chang, K.I., Bowyer, K.W., Flynn, P.J.: An Evaluation of Multi-modal 2D+3D Face Biometrics. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)

    Google Scholar 

  36. González-Jiménez, D., Bicego, M., Tangelder, J.W.H., Schouten, B.A.M., Ambekar, O., Alba-Castro, J.L., Grosso, E., Tistarelli, M.: Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 474–483. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  37. Bicego, M., Brelstaff, G., Brodo, L., Grosso, E., Lagorio, A., Tistarelli, M.: Distinctiveness of faces: a computational approach. ACM Transactions on Applied Perception 5(2) (2008)

    Google Scholar 

  38. Kisku, D.R., Rattani, A., Grosso, E., Tistarelli, M.: Face Identification by SIFT-based Complete Graph Topology. In: Proceedings of IEEE Int.l Workshop on Automatic Identification Advanced Technologies (AutoId 2007), Alghero, pp. 69–73, June 7-8 (2007)

    Google Scholar 

  39. Rattani, A., Kisku, D.R., Bicego, M., Tistarelli, M.: Feature Level Fusion of Face and Fingerprint Biometrics. In: Proc. of first IEEE Int.l Conference on Biometrics: Theory, Applications and Systems (BTAS 2007), Washington DC, September 27-29 (2007)

    Google Scholar 

  40. Gordon, G., Lewis, M.: Face Recognition Using Video Clips and Mug Shots. In: Proceedings of the Office of National Drug Control Policy (ONDCP) International Technical Symposium (Nashua, NH) (October 1995)

    Google Scholar 

  41. Li, Y., Gong, S., Liddell, H.: Video-based online face recognition using identity surfaces. In: Proceedings IEEE International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Vancouver, Canada, pp. 40–46 (July 2001)

    Google Scholar 

  42. Li, Y., Gong, S., Liddell, H.: Modelling faces dynamically across views and over time. In: Proceedings IEEE International Conference on Computer Vision, Vancouver, Canada, pp. 554–559 (July 2001)

    Google Scholar 

  43. Lucas, S.M.: Continuous n-tuple classifier and its application to real-time face recognition. In: IEE Proceedings-Vision Image and Signal Processing, vol. 145(5), p. 343 (October 1998)

    Google Scholar 

  44. Lucas, S.M., Huang, T.K.: Sequence recognition with scanning N-tuple ensembles. In: Proceedings ICPR 2004 (III), pp. 410–413 (2004)

    Google Scholar 

  45. Eickeler, S., Müller, S., Rigoll, G.: Recognition of JPEG compressed face images based on statistical methods. Image and Vision Computing 18(4), 279–287 (2000)

    Article  Google Scholar 

  46. Raytchev, B., Murase, H.: Unsupervised recognition of multi-view face sequences based on pairwise clustering with attraction and repulsion. Computer Vision and Image Understanding 91(1–2), 22–52 (2003)

    Article  Google Scholar 

  47. Raytchev, B., Murase, H.: VQ-Faces: Unsupervised Face Recognition from Image Sequences. In: Proceedings ICIP 2002 (II), pp. 809–812 (2002)

    Google Scholar 

  48. Raytchev, B., Murase, H.: Unsupervised Face Recognition from Image Sequences. In: Proceedings ICIP 2001(I), pp. 1042–1045 (2001)

    Google Scholar 

  49. Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91(1–2), 214–245 (2003)

    Article  Google Scholar 

  50. Zhou, S., Krueger, V., Chellappa, R.: Face Recognition from Video: A Condensation Approach. In: Proceedings IEEE AFGR 2002, pp. 212–217 (2002)

    Google Scholar 

  51. Zhou, S., Chellappa, R.: Probabilistic Human Recognition from Video. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, p. 681. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  52. Zhou, S., Chellappa, R.: A robust algorithm for probabilistic human recognition from video. In: Proceedings ICPR 2002 (I), pp. 226–229 (2002)

    Google Scholar 

  53. Zhou, S., Chellappa, R.: Rank constrained recognition under unknown illuminations. In: Proceedings AMFG 2003, pp. 11–18 (2003)

    Google Scholar 

  54. Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters. Image Processing 13(11), 1491–1506 (2004)

    Article  Google Scholar 

  55. Zhou, S.K., Chellappa, R., Moghaddam, B.: Intra-personal kernel space for face recognition. In: Proceedings IEEE AFGR 2004, pp. 235–240 (2004)

    Google Scholar 

  56. Zhou, S.K., Chellappa, R.: Multiple-exemplar discriminant analysis for face recognition. In: Proceedings ICPR 2004 (IV), pp. 191–194 (2004)

    Google Scholar 

  57. Zhou, S.K., Chellappa, R.: Probabilistic identity characterization for face recognition. In: Proceedings CVPR 2004 (II), pp. 805–812 (2004)

    Google Scholar 

  58. Howell, A.J., Buxton, H.: Towards Unconstrained Face Recognition from Image Sequences. In: Proceeding. of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR 1996), Killington, VT, pp. 224–229 (1996)

    Google Scholar 

  59. Li, Y., Gong, S., Liddell, H.: Support Vector Regression and Classification Based Multiview Face Detection and Recognition. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2000), Grenoble, France, pp. 300–305 (2000)

    Google Scholar 

  60. Roli, F., Kittler, J. (eds.): MCS 2002. LNCS, vol. 2364. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  61. Achermann, B., Bunke, H.: Combination of Classifiers on the Decision Level for Face Recognition. Technical Report IAM-96-002, Institut für Informatik und angewandte Mathematik, Universität Bern (January 1996)

    Google Scholar 

  62. Mou, D., Schweer, R., Rothermel, A.: Automatic Databases for Unsupervised Face Recognition. In: Proceedings FaceVideo 2004, p. 90 (2004)

    Google Scholar 

  63. Song, X., Lin, C.Y., Sun, M.T.: Cross-Modality Automatic Face Model Training from Large Video Databases. In: Proceedings FaceVideo 2004, p. 91 (2004)

    Google Scholar 

  64. Arandjelovic, O., Cipolla, R.: Face Recognition from Face Motion Manifolds using Robust Kernel Resistor-Average Distance. In: Proceedings FaceVideo 2004, p. 88 (2004)

    Google Scholar 

  65. Aggarwal, G., Chowdhury, A.K.R., Chellappa, R.: A system identification approach for video-based face recognition. In: Proceedings ICPR 2004 (IV), pp. 175–178 (2004)

    Google Scholar 

  66. Matsui, A., Clippingdale, S., Uzawa, F., Matsumoto, T.: Bayesian face recognition using a Markov chain Monte Carlo method. In: Proceedings ICPR 2004 (III), pp. 918–921 (2004)

    Google Scholar 

  67. Clippingdale, S., Fujii, M.: Face recognition for video indexing: Randomization of face templates improves robustness to facial expression. In: García, N., Salgado, L., Martínez, J.M. (eds.) VLBV 2003. LNCS, vol. 2849, pp. 32–40. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  68. Clippingdale, S., Ito, T.: A Unified Approach to Video Face Detection, Tracking and Recognition. In: Proceedings ICIP 1999 (I), pp. 662–666 (1999)

    Google Scholar 

  69. Roark, D.A., O’Toole, A.J., Abdi, H.: Human recognition of familiar and unfamiliar people in naturalistic video. In: Proceedings AMFG 2003, pp. 36–41 (2003)

    Google Scholar 

  70. Gorodnichy, D.O.: Facial Recognition in Video. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 505–514. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  71. Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.J.: Video-based face recognition using probabilistic appearance manifolds. In: Proceedings CVPR 2003 (I), pp. 313–320 (2003)

    Google Scholar 

  72. Liu, X., Chen, T.: Video-based face recognition using adaptive hidden Markov models. In: Proceedings CVPR 2003 (I), pp. 340–345 (2003)

    Google Scholar 

  73. Huang, K.S., Trivedi, M.M.: Streaming face recognition using multicamera video arrays. In: Proceedings ICPR 2002 (IV), pp. 213–216 (2002)

    Google Scholar 

  74. Gross, R., Brajovic, V.: An Image Preprocessing Algorithm for Illumination Invariant Face Recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 10–18. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  75. Gross, R., Yang, J., Waibel, A.: Growing Gaussian Mixture Models for Pose Invariant Face Recognition. In: Proceedings ICPR 2000 (I), pp. 1088–1091 (2000)

    Google Scholar 

  76. Krüger, V., Gross, R., Baker, S.: Appearance-Based 3-D Face Recognition from Video. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, p. 566. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  77. Krüger, V., Zhou, S.: Exemplar-Based Face Recognition from Video. In: Proceedings, IEEE AFGR 2002, pp. 175–180 (2002)

    Google Scholar 

  78. Shakhnarovich, G., Fisher III, J.W., Darrell, T.: Face recognition from long-term observations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 851–865. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  79. Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: Proceedings IEEE AFGR 2002, pp. 14–21 (2002)

    Google Scholar 

  80. Li, Y., Gong, S., Liddell, H.: Video-based online face recognition using identity surfaces. In: Proceedings IEEE ICCV Workshop on RATFG 2001, pp. 40–46 (2001)

    Google Scholar 

  81. Weng, J., Evans, C.H., Hwang, W.S.: An Incremental Learning Method for Face Recognition under Continuous Video Stream. In: Proceedings IEEE AFGR 2000, pp. 251–256 (2000)

    Google Scholar 

  82. Ho, P.: Rotation Invariant Real-time Face Detection and Recognition System. MIT-AI Memo 2001-010, May 31 (2001)

    Google Scholar 

  83. Yamaguchi, O., Fukui, K., Maeda, K.: Face Recognition Using Temporal Image Sequence. In: Proceedings IEEE AFGR 1998, pp. 318–323 (1998)

    Google Scholar 

  84. Nagao, K., Sohma, M.: Weak Orthogonalization of Face and Perturbation for Recognition. In: Proceedings CVPR 1998, pp. 845–852 (1998)

    Google Scholar 

  85. Nagao, K., Sohma, M.: Recognizing faces by weakly orthogonalizing against perturbations. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 613. Springer, Heidelberg (1998)

    Google Scholar 

  86. Edwards, G.J., Taylor, C.J., Cootes, T.F.: Improving Identification Performance by Integrating Evidence from Sequences. In: Proceedings CVPR 1999 (I), pp. 486–491 (1999)

    Google Scholar 

  87. Cootes, T.F., Wheeler, G.V., Walker, K., Taylor, C.J.: Coupled-View Active Appearance Models. In: Proceedings BMVC 2000, pp. 52–61 (2000)

    Google Scholar 

  88. Edwards, G.J., Taylor, C.J., Cootes, T.F.: Learning to Identify and Track Faces in Image Sequences. In: Proceedings IEEE AFGR 1998, pp. 260–265 (1998)

    Google Scholar 

  89. Déniz, M.C., Lorenzo, J., Hernández, M.: An incremental learning algorithm for face recognition. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 1–9. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  90. Fukui, K., Yamaguchi, O.: Face recognition using multiviewpoint patterns for robot vision. In: Proceedings International Symposium of Robotics Research (2003)

    Google Scholar 

  91. Bicego, M., Grosso, E., Tistarelli, M.: Person authentication from video of faces: a behavioral and physiological approach using Pseudo Hierarchical Hidden Markov Models. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 113–120. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  92. Tistarelli, M., Bicego, M., Grosso, E.: Dynamic face recognition: From human to machine vision. In: Tistarelli, M., Bigun, J. (eds.) Image and Vision Computing: Special issue on Multimodal Biometrics (2007)doi:10.1016/j.imavis.2007.05.006.

    Google Scholar 

  93. Phillips, J.J., Flynn, P., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Jaesik, M., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proceedings CVPR 2005, pp. 947–954 (2005)

    Google Scholar 

  94. Phillips, J.J., Flynn, P., Scruggs, T., Bowyer, K.W., Worek, W.: Preliminary Face Recognition Grand Challenge Results. In: Proceedings 7th International Conference on Automatic Face and Gesture Recognition, pp. 15–24 (2006)

    Google Scholar 

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Authors and Affiliations

  1. Computer Vision Laboratory, Università di Sassari, Alghero, Italy

    Massimo Tistarelli & Enrico Grosso

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  1. Massimo Tistarelli
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  2. Enrico Grosso
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Editors and Affiliations

  1. CWI/Fontys, 5600 AH, Eindhoven, The Netherlands

    Ben Schouten

  2. Roskilde University, 4000, Roskilde, Denmark

    Niels Christian Juul

  3. Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, Switzerland

    Andrzej Drygajlo

  4. University of Sassari, 07041, Alghero, Italy

    Massimo Tistarelli

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Tistarelli, M., Grosso, E. (2008). Identity Management in Face Recognition Systems. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds) Biometrics and Identity Management. BioID 2008. Lecture Notes in Computer Science, vol 5372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89991-4_8

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  • DOI: https://doi.org/10.1007/978-3-540-89991-4_8

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