Fundamentals and Advances in Biometrics and Face Recognition

  • Dengpan Mou

Abstract

In this chapter, we mainly focus on the fundamentals and advances in the research of biometric recognition. In section 1, generalized biometric procedures and categories are defined and overviewed. This is followed in section 2 by the brief introduction and surveys on current cognitive science research. The essential point here is the fundamental intelligence of human brains. In section 3, machine-based biometric recognition tasks and methods are explored. As the marketing leader, fingerprint recognition is exclusively discussed in more details. Section 4 to 7 explores state-of-the-art research and limitations in machine-based face recognition, in video base-face recognition and in unsupervised recognition systems. This chapter ends by the summary as well as further thoughts inspired from both cognitive and machine recognition research.

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References

  1. [1]
    H. Chen, and A. K. Jain: Dental Biometrics: Alignment and Matching of Dental Radiographs. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, 2005, pp. 1319–1326CrossRefGoogle Scholar
  2. [2]
    D. Lazer, Ed.: DNA and the Criminal Justice System: The Technology of Justice, MIT Press, Cambridge, MA, 2004Google Scholar
  3. [3]
    D. A. Norman: Twelve Issues for Cognitive Science. Cognitive Science, Vol. 4, Issue 1, 1980, pp. 1–32CrossRefMathSciNetGoogle Scholar
  4. [4]
    Halfon N, Shulman E, and Hochstein M, eds.: Brain Development in Early Childhood. Building Community Systems for Young Children, UCLA Center for Healthier Children, Families and Communities, 2001Google Scholar
  5. [5]
    J.P. de Magalhaes, and A. Sandberg: Cognitive aging as an extension of brain development: A model linking learning, brain plasticity, and neurodegeneration. Mechanisms of Ageing and Development, Vol. 126, 2005, pp. 1026–1033CrossRefGoogle Scholar
  6. [6]
    G.M. Shepherd: The Synaptic Organization of the Brain 5th Edition, Oxford, Oxford Univ. Press, 2004, p.6Google Scholar
  7. [7]
    C. Koch: Biophysics of Computation. Information Processing in Single Neurons, New York, Oxford Univ. Press, 1999, p.87Google Scholar
  8. [8]
    Henry Gray: Anatomy of the Human Body, 1918Google Scholar
  9. [9]
    R. S. Michalski., G. Carbonell, and T. M. Mitchell: Machine Learning: An Artificial Intelligence Approach, Berlin, Springer-Verlag, 1984Google Scholar
  10. [10]
    P. Thagard: Mind: Introduction to Cognitive Science, 2nd Edition. Cambridge, The MIT Press, 2005Google Scholar
  11. [11]
    B.A. Wandell: What’s in your mind? Nature Neuroscience, Vol. 11, No. 4, 2008Google Scholar
  12. [12]
    B.A. Wandell, S.O. Dumoulin, and A. A. Brewer: Visual Field Maps in Human Cortex. Neuron, Vol. 56, No. 2, 2007Google Scholar
  13. [13]
    T. Serre, L. Wolf, et al.: Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, 2007, pp. 411–426CrossRefGoogle Scholar
  14. [14]
    L. Wiskott: How does our visual system achieve shift and size invariance? In: J.L. van Hemmen and T.J. Sejnowski, 23 Problems in Systems Neuroscience. Oxford, Oxford University Press, 2006Google Scholar
  15. [15]
    J. Mutch and D. Lowe: Multiclass object recognition using sparse, localized features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006Google Scholar
  16. [16]
    M. Ranzato, F. Huang, et al: Unsupervised learning of invariant feature hierarchies, with application to object recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007Google Scholar
  17. [17]
    D. Mou: Autonomous Face Recognition. Ph.D Dissertation, http://vts.uni-ulm.de/query/longview.meta.asp?document_id=5370, accessed 28 October 2005
  18. [18]
    M. Johnson, S. Dziurawiec, et al.: Newborns preferential tracking of face-like stimuli and its subsequent decline. Cognition, Vol. 40, 1991, pp. 1–19CrossRefGoogle Scholar
  19. [19]
    J. Sergent, S. Ohta, and B. MacDonald: Functional neuroanatomy of face and object processing: a positron emission tomography study. Brain, Vol. 15, No. 1, 1992, pp. 15–36CrossRefGoogle Scholar
  20. [20]
    N. Kanwisher, J. McDermott, and M. M. Chun: The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. The Journal of Neuroscience, Vol. 17, No. 11, 1997, pp. 4302–4311Google Scholar
  21. [21]
    J. V. Haxby, E. A. Hoffman, and M. I. Gobbini: The distributed human neural system for face perception. Trends in Cognitive Sciences, Vol. 4, Issue 6, 2000, pp. 223–231CrossRefGoogle Scholar
  22. [22]
    E. H. Aylward, J. E. Park, et al.: Brain Activation during Face Perception: Evidence of a Developmental Change. Journal of Cognitive Neuroscience, Vol. 17, Issue 2, 2005Google Scholar
  23. [23]
    N. Kanwisher, and G. Yovel: The Fusiform Face Area: A Cortical Region Specialized for the Perception of Faces. Philosophical Transactions of the Royal Society of London B: Biological Sciences, Vol. 361, 2006, pp. 2109–2128CrossRefGoogle Scholar
  24. [24]
    M. J. Tarr, and I. Gauthier: FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise. Nature Neuroscience, Vol. 3, No. 8, 2000Google Scholar
  25. [25]
    I. Gauthier, and N. K. Logothetis: Is face recognition not so unique, after all? Cognitive Neuropsychology, Vol. 17, 2000, pp. 125–142CrossRefGoogle Scholar
  26. [26]
    M. Riesenhuber and T. Poggio: Neural mechanisms of object recognition. Current Opinion in Neurobiology, Vol. 12, 2002, pp. 162–168CrossRefGoogle Scholar
  27. [27]
    T. J. Andrews and D. Schluppeck: Neural responses to Mooney images reveal a modular representation of faces in human visual cortex. Neuroimage, Vol. 21, Issue 1, 2004Google Scholar
  28. [28]
    P. Rotshtein, R. N. Henson, et al.: Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain. Nature Neuroscience, Vol. 8, No. 1, 2005Google Scholar
  29. [29]
    C. G. Gross: Representation of visual stimuli in inferior temporal cortex. Philosophical Transactions of the Royal Society of London B., Vol. 335, 1992, pp. 3–10CrossRefGoogle Scholar
  30. [30]
    A. Mechelli, C.J. Price, et al.: Where bottom-up meets top-down: neuronal interactions during perception and imagery. Cerebral Cortex, Vol. 14, No. 11, 2004Google Scholar
  31. [31]
    M.R. Johnson, K.J. Mitchell, et al.: A brief thought can modulate activity in extrastriate visual areas: Top-down effects of refreshing just-seen visual stimuli. Neuroimage, Vol. 37, Issue 1, 2007Google Scholar
  32. [32]
    P. Sinha, B. Balas, et al.: Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About. Proceedings of The IEEE, Vol. 94, No. 11, 2006Google Scholar
  33. [33]
    M. Dawson: Understanding Cognitive Science. Malden, Blackwell, 1998Google Scholar
  34. [34]
    A A. Ross, K. Nandakumar and A.K. Jain: Handbook of Multibiometrics. Boston, Springer, 2006Google Scholar
  35. [35]
    H. Chen, and B. Bhanu: Human Ear Recognition in 3D. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, 2007, pp. 718–737CrossRefGoogle Scholar
  36. [36]
    Z. Korotkaya: Biometrics Person Authentication: Odor. <http://www.it.lut.fi/kurssit/>03-04/010970000/ seminars/Korotkaya.pdf, accessed 08 December 2005
  37. [37]
    R. Palaniappan, and D. P. Mandic: Biometrics from Brain Electrical Activity: A Machine Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, 2007, pp. 738–742CrossRefGoogle Scholar
  38. [38]
  39. [39]
    Information Technology. Biometric Data Interchange Formats. Iris Image Data. ISO/IEC 19794-6:2005Google Scholar
  40. [40]
    Faulds, Henry: On the Skin-furrows of the Hand. Nature, Macmillan and Co., London, October 28, 1880, p. 605Google Scholar
  41. [41]
    Herschel, W. J.: Skin Furrows of the Hand. Nature, Macmillan and Co., London, Nov. 25, 1880, p. 76Google Scholar
  42. [42]
    Galton, Sir Francis: Finger Prints. Macmillan and Co., London, 1892.Google Scholar
  43. [43]
    <http://www.fbi.gov/hq/cjisd/iafis.htm>, accessed 15 December 2007
  44. [44]
    D Maltoni, D Maio, et al.: Handbook of Fingerprint Recognition, New York, Springer, 2003MATHGoogle Scholar
  45. [45]
    E. Hjelmas and B.K. Low: Face Detection: A Survey. Computer Vision and Image Understanding, 2001, Vol. 83, No. 3, 2001, pp. 236–274MATHCrossRefGoogle Scholar
  46. [46]
    M. Yang, D.J. Kriegman, and N. Ahuja: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, 2002, pp. 34–58CrossRefGoogle Scholar
  47. [47]
    M. Yang: Recent Advances in Face Detection. IEEE ICIP 2004 Tutorial, Cambridge, UK, <http://vision.ai.uiuc.edu/mhyang/face-detection-survey.html>, accessed 13 October 2005
  48. [48]
    S. Gong, S. McKenna, and A. Psarrou: Dynamic Vision: From Images to Face Recognition, London, Imperial College Press, 2000Google Scholar
  49. [49]
    K. C. Yow and R. Cipolla: Feature-Based Human Face Detection. Image and Vision Computing, Vol. 15, No. 9, 1997, pp. 713–735CrossRefGoogle Scholar
  50. [50]
    J. Yang and A. Waibel, “A Real-Time Face Tracker”, Proceedings of the 3rd Workshop on Applications of Computer Vision (WACV’96), 1996, pp. 142–147Google Scholar
  51. [51]
    G. Yang and T. Huang: Human Face Dtection in Complex Background. Pattern Recognition, Vol. 27, No. 1, 1994, pp. 53–63CrossRefGoogle Scholar
  52. [52]
    C. Kotropoulos and I. Pitas: Rule-Based Face Detection in Frontal Views. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’97), Vol. 4, 1997, pp. 2537–2540Google Scholar
  53. [53]
    B. Cumbers (2003): Passive Biometric Customer Identification and Tracking System. U.S. Patent, 6554705, April 2003Google Scholar
  54. [54]
    K. Sung and T. Poggio: Example-Based learning for view-Based Human Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 39–51CrossRefGoogle Scholar
  55. [55]
    H. Rowley, S. Baluja, and T. Kanade: Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 23–38CrossRefGoogle Scholar
  56. [56]
    R, Fér aud, O. Bernier, et al.: A Fast and Accurate Face Detector Based on Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2001, pp. 42–53CrossRefGoogle Scholar
  57. [57]
    E. Osuna, R. Freund, and F. Girosi: Training Support Vector Machines: An Application to Face Detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 130–136Google Scholar
  58. [58]
    H. Schneiderman and T. Kanade: A Statistical Method for 3D Object Detection Applied to Faces and Cars. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, 2000, pp. 746–751Google Scholar
  59. [59]
    M. Yang, D. Roth, and N. Ahuja: A SnoW-Based Linear Subspaces for Face Detection. In: S. Solla, T. Leen, and K. Müller, eds. Advances in Neural Information Processing System 12, MIT Press, 2000, pp. 855–861Google Scholar
  60. [60]
    P. Viola and M. Jones: Robust Real-time Object Detection. IEEE ICCV Workshop on Statistical and Computational Theories of Vision, July 13, 2001Google Scholar
  61. [61]
    M. Jones, P. Viola: Fast Multi-view Face Detection. Mitsubishi Electric Research Laboratories Technical Reports, TR2003-96, 2003, <http://www>. merl.com/ reports/ docs/ TR2003-96.pdf, accessed 12 October 2005
  62. [62]
    Z. Zhang, L. Zhu, et al.: Real-Time Multi-View Face Detection. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May 2002, pp. 149–154Google Scholar
  63. [63]
    M. Turk and A. Pentland: Eigenfaces for Recognition. Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 72–86CrossRefGoogle Scholar
  64. [64]
    S. Palanive, B.S. Venkatesh, and B. Yegnanarayana: Real time face recognition system using autoassociative neural network models. IEEE Conference Proceedings on Acoustics, Speech, and Signal Processing (ICASSP′03), Vol. 2, 2003, pp. 833–836Google Scholar
  65. [65]
    T. kim, S. Lee, et al.: Integrated approach of multiple face detection for video surveillance. Proceedings of IEEE 16th Conference on Pattern Recognition, Vol. 2, 2002, pp. 394–397Google Scholar
  66. [66]
    D. Butler, C. McCool, et al.: Robust Face Localisation Using Motion. Colour & Fusion Proceedings of Digital Image Computing: Techniques and Applications (DICTA 2003), 2003, pp. 899–908Google Scholar
  67. [67]
    C. Wren, A. Azerbayejani, et al.: Pfinder: A Real-Time Tracking of Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 780–785CrossRefGoogle Scholar
  68. [68]
    Y. Raja, S.J. McKenna, and S. Gong: Tracking and Segmenting People in Varying Lighting Conditions Using Color. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, 1998, pp. 228–233Google Scholar
  69. [69]
    K. Schwerdt and J. Crowley: Robust Face Tracking Using Colour. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, 2000, pp. 90–95Google Scholar
  70. [70]
    S. Birchfield: Elliptical Head Tracking Using Intensity Gradients and Color Histograms. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1998, pp. 232–237Google Scholar
  71. [71]
    R.C. Verma, C. Schmid, and K. Mikolajczyk: Face detection and tracking in a video by propagating detection probabilities. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, Issue 10, 2003, pp. 1215–1228CrossRefGoogle Scholar
  72. [72]
    R. Chellappa, C.L. Wilson and S. Sirohey: Human and Machine Recognition of Faces: A Survey. Proceedings of IEEE, Vol. 83, No. 5, 1995, pp. 705–740CrossRefGoogle Scholar
  73. [73]
    W. Zhao, R. Chellappa, et al.: Face Recognition: A Literature Survey. Technical Report (CS-TR-4167R), University of Maryland. <ftp://ftp.cfar.umd.edu>, accessed 20 August 2005
  74. [74]
    K. Bowyer, K. Chang, and P. Flynn: A survey of Approaches and Challenges in 3D and Multi-Modal 3D 2D Face Recognition. IEEE Transactions on Computer Vision and Image Understanding, Vol. 101, No. 1, 2006, pp. 1–15CrossRefGoogle Scholar
  75. [75]
    X. Lu, and A. Jain: Deformation Modeling for Robust 3D Face Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 8, 2008, pp. 1346–1357CrossRefGoogle Scholar
  76. [76]
    A. Franco, D. Maio and D. Maltoni: 2D Face Recognition based on Supervised Subspace Learning from 3D Models. Pattern Recognition, Vol. 41, No. 12, 2008, pp. 3822–3833MATHCrossRefGoogle Scholar
  77. [77]
    P. J. Phillips, P. Rauss and S. Der: FERET (Face Recognition Technology) Recognition Algorithm Development and Test Report. Technical Report ARL-TR 995, U.S. Army Research Laboratory, 1996Google Scholar
  78. [78]
    P. J. Phillips, H. Moon, et al.: The FERET Evaluation Method for Face Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, 2000, pp. 1090–1104CrossRefGoogle Scholar
  79. [79]
    D. M. Blackburn, M. Bone and P.J. Phillips: FRVT 2000 Evaluation Report. Technical Report, Feb. 16th, 2001, <http://www.frvt.org>, accessed 29 March 2007
  80. [80]
    P. J. Phillips, P. Grother, et al.: FRVT 2002 Evaluation Report, Technical Report, March, 2003, <http://www.frvt.org>, accessed 29 March 2007
  81. [81]
    P. J. Phillips, W. T. Scruggs, et al.: FRVT 2006 and ICE 2006 Large-Scale Results. Technical Report, March 2007, <http://www.frvt.org>, accessed 29 March 2007
  82. [82]
    V. Blanz, S. Romdhami, and T. Vetter: Face identification across different poses and illuminations with a 3D morphable model. Proceedings of International Conference on Automatic Face and Gesture Recognition, 2002, pp. 202–207Google Scholar
  83. [83]
    V. Blanz and T. Vetter: Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 25, 2003, pp. 106–1074CrossRefGoogle Scholar
  84. [84]
    V. Bruce: Recognizing Faces. London, Lawrence Erlbaum Associates, 1988Google Scholar
  85. [85]
    V. Bruce, P.J.B. Hancock, and A.M. Burton: Human Face Perception and Identification. In: Face Recognition: From Theory to Applications, Berlin, Springer-Verlag, 1998, pp. 51–72Google Scholar
  86. [86]
    M.S, Bartlett, J.R. Movellan and T.J. Sejnowski: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks, Vol. 13, No. 6, 2002, pp. 1450–1464CrossRefGoogle Scholar
  87. [87]
    D.L. Swets and J.J. Weng: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, 1996, pp. 831–836CrossRefGoogle Scholar
  88. [88]
    P. Belhumeur, J.P. Hespanha, and D.J. Kriegman: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 711–720CrossRefGoogle Scholar
  89. [89]
    A.V. Nefian and H.H. Hayes III: Hidden Markov Models for Face Recognition. IEEE International Conference on Acoustic, Speech and Signal Processing, Vol. 5, 1998, pp. 2721–2724Google Scholar
  90. [90]
    V. V. Kohir and U. B. Desai: Face recognition using DCT-HMM approach. Workshop on Advances in Facial Image Analysis and Recognition Technology (AFIART), June 1998Google Scholar
  91. [91]
    R. Tjahyadi, W. Liu, and S. Venkatesh: Application of the DCT Energy Histogram for Face Recognition. Proceedings of the 2nd International Conference on Information Technology for Application (ICITA 2004), 2004, pp. 305–310Google Scholar
  92. [92]
    H. Kang, T. F. Cootes, and C. J. Taylor: A comparison of face verification algorithms using appearance models. Proceedings of The British Machine Vision Conference, Vol. 2, 2002, pp. 477–486Google Scholar
  93. [93]
    X. Lu, Y. Wang, and A. K. Jain: Combining classifiers for face recognition. Proceedings of the IEEE International Conference on Multimedia & Expo, Vol. 3, July 2003, pp. 13–16Google Scholar
  94. [94]
    R. Singh, M. Vatsa, et al.: A Mosaicing Scheme for Pose Invariant Face Recognition. IEEE Transactions on Systems, Mans and Cybernetics-B, Special Issue on Biometrics, Vol. 37, Issue 5, 2007, pp. 1212–1225CrossRefGoogle Scholar
  95. [95]
    M. Bicego, U. Castellani, V. Murino: Using Hidden Markov Models and Wavelets for face recognition. Proceedings of IEEE International Conference on Image Analysis and Processing (ICIAP03), 2003, pp. 52–56Google Scholar
  96. [96]
    L. Wiskott, J. Fellous, et al.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 775–779CrossRefGoogle Scholar
  97. [97]
    C. Liu and H. Wechsler: A Gabor Feature Classifier for Face Recognition. Proceedings of Eighth IEEE International Conference on Computer Vision, Vol. 2, 2001, pp. 270–275CrossRefGoogle Scholar
  98. [98]
    B.A. Draper, K. Baek, et al.: Recognizing faces with PCA and ICA. Computer Vision and Image Understanding, Vol. 91, No. 1, 2003, pp. 115–137CrossRefGoogle Scholar
  99. [99]
    A.M. Martinez and A.C. Kak: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, 2001, pp. 228–233CrossRefGoogle Scholar
  100. [100]
    M.H. Yang: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FG’02), May 2002, pp. 215–220Google Scholar
  101. [101]
    J. Lu, K.N.Plataniotis, and A.N. Venetsanopoulos: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Transactions on Neural Networks, Vol. 14, No. 1, 2003, pp. 117–126CrossRefGoogle Scholar
  102. [102]
    Y. Zhang, L. Lang and O. Hamsici: Subspace Analysis for Facial Image Recognition: A Comparative Study. <http://www.stat.ohio-state.edu/~goel/> STATLEARN/, accessed 12 October 2006.
  103. [103]
    G. Guo, S. Z. Li, and C. Kapluk: Face recognition by support vector machines. Image and Vision Computing, Special Issue on Artificial Neural Networks for Image Analysis and Computer Vision, Vol. 19, No. 9-10, 2001, pp. 631–638Google Scholar
  104. [104]
    B. Heisele, P. Ho and T. Poggio: Face Recognition with Support Vector Machines: Global versus Component-based Approach. Proceedings of IEEE International Conference on Computer Vision, 2001, pp. 688–694Google Scholar
  105. [105]
    J. Huang, V. Blanz, and B. Heisele: Face Recognition Using Component-Based SVM Classification and Morphable Models. SVM 2002, 2002, pp. 334–341Google Scholar
  106. [106]
    S. Lawrence, C.L. Giles, et al.: Face Recognition: A Convolutional Neural Network Approach. IEEE Transactions on Neural Networks, Vol. 8, No. 1, 1997, pp. 98–113CrossRefGoogle Scholar
  107. [107]
    T. Kurita, M. Pic, and T. Takahashi: Recognition and Detection of Occluded Faces by A Neural Network Classifier with Recursive Data Reconstruction. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’03), 2003, pp. 53–58Google Scholar
  108. [108]
    Xiaoming Liu, and Tsuhan Chen: Video-Based Face Recognition Using Adaptive Hidden Markov Models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’03), 2003, pp. 340–345Google Scholar
  109. [109]
    V. Krueger and S. Zhou: Exemplar-based Face Recognition from Video. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May 21–22, 2002, pp. 175–180Google Scholar
  110. [110]
    A: Hadid and M. Pietikäinen: From Still Image to Video-Based Face Recognition: An Experimental Analysis. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), 2004, pp. 813–818Google Scholar
  111. [111]
    X. Tang and Z. Li: Video Based Face Recognition Using Multiple Classifiers. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), 2004, pp. 345–349Google Scholar
  112. [112]
    O. Arandjelovic and R. Cipolla: Face Recognition from Face Motion Manifolds using Robust Kernel Register-Average Distance. IEEE International Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04), Vol. 5, 2004, p.70Google Scholar
  113. [113]
    J. Weng and W. Hwang: Toward Automation of Learning: The State Self-Organization Problem for a Face Recognizer. Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, 1998, pp. 384–389Google Scholar
  114. [114]
    J. Weng, C. Evans, and W. Hwang: An Incremental Learning Method for Face Recognition under Continuous Video Stream. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 251–256Google Scholar
  115. [115]
    K. Okada, L. Kite, and C. von der Malsburg: An Adaptive Person Recognition System. Proceedings of the IEEE International Workshop on Robot-Human Interactive Communication, 2001, pp. 436–441Google Scholar
  116. [116]
    Lijin Aryananda: Recognizing and Remembering Individuals: Online and Unsupervised Face Recognition for Humanoid Robot. Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Vol. 2, 2002, pp. 1202–1207CrossRefGoogle Scholar
  117. [117]
    B. Raytchev, H. Murase: Unsupervised Face Recognition by Associative Chaining. Pattern Recognition, Vol. 36, No. 1, 2003, pp. 245–257MATHCrossRefGoogle Scholar
  118. [118]
    Q. Xiong and C. Jaynes: Mugshot Database Acquisition in Video Surveillance Networks Using Incremental Auto-Clustering Quality Measures. Proceedings of the 2003 IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’03), 2003, pp. 191–198Google Scholar
  119. [119]
    H. Wechsel, V. Kakkad, et al.: Automatic Video-Based Person Authentication Using the RBF Network. Proceedings of 1st International Conference on Audio And Videobased Biometric Person Authentication, 1997, pp. 85–92Google Scholar
  120. [120]
    C. Lambert (1991): Autonomous Face Recognition Machine. U.S. Patent, 5012522, April, 1991Google Scholar
  121. [121]
    Y.T. Lin (2002): Adaptive Facial Recognition System and Method. U.S. Patent application publication, US2002/0136433, Sep. 26, 2002Google Scholar
  122. [122]
    J.L. Center JR (2003).: Real-time Facial Recognition and Verification System. U.S. Patent application publication, US2003/0059124, Mar. 27, 2003Google Scholar

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© Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dengpan Mou
    • 1
  1. 1.Harman/Becker Automotive Systems GmbHD-76307KarlsbadGermany

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