Skip to main content
SpringerLink
Log in

Face Recognition

  • Chapter
Biometric Systems

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Agresti (ed.), Categorical Data Analysis. John Wiley & Sons, 1990.

    Google Scholar 

  2. J. J. Atick, P. A. Griffin and A. N. Redlich, Statistical approach to shape from shading: reconstruction of three-dimensional face surfaces from single two-dimensional images. Neural Computation, 8, 1321–1340, 1996.

    PubMed  Google Scholar 

  3. H. B. Barlow, Possible principles underlying the transformation of sensory messages. In W. Rosenblith (ed.), Sensory Communication, pp. 217–234. MIT Press, 1961.

    Google Scholar 

  4. H. B. Barlow, Unsupervised learning. Neural Computation, 1, 295–311, 1989.

    Google Scholar 

  5. H. B. Barlow, T. P. Kaushal and G. J. Mitchison, Finding minimum entropy codes. Neural Computation, 1(3), 412–423, 1989.

    Google Scholar 

  6. P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 711–720, 1997.

    Article  Google Scholar 

  7. A. J. Bell and T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129–1159, 1995.

    PubMed  Google Scholar 

  8. D. Beymer, Vectorizing face images by interleaving shape and texture computations. A.I. memo No. 1537, Artificial Intelligence Laboratory, MIT, 1995.

    Google Scholar 

  9. D. M. Blackburn, J. M. Bone and P. J. Phillips, FRVT 2000 evaluation report. Technical report,February, 2001. Available online at http://www.frvt.org/.

    Google Scholar 

  10. V. Blanz and T. Vetter, A morphable model for the synthesis of 3D faces. Proc. SIGGRAPH 99, 1999.

    Google Scholar 

  11. R. Brunelli and T. Poggio, Face recognition: features vs. templates. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(10), 1042–1053, 1993.

    Article  Google Scholar 

  12. R. Chellappa, C. L. Wilson and S. Sirohey, Human and machine recognition of faces: a survey. Proc. IEEE, 83(5), 705–740, 1995.

    Article  Google Scholar 

  13. P. Comon, Independent component analysis, a new concept? Signal Processing, 36, 287–314, 1994.

    Article  Google Scholar 

  14. T. F. Cootes, G. J. Edwards and C. J. Taylor, Active appearance models. Proc. Fifth European Conference on Computer Vision, University of Freiburg, Germany, June 2–6, 1998.

    Google Scholar 

  15. I. Craw, N. Costen, T. Kato and S. Akamatsu, How should we represent faces for automatic recognition? IEEE Trans. Pattern Analysis and Machine Intelligence, 21(8), 725–736, 1999.

    Article  Google Scholar 

  16. I. Craw and D. Tock. The computer understanding of faces. In V. Bruce and M. Burton (eds), Processing Images of Faces. Ablex Publishing Corporation, 1992.

    Google Scholar 

  17. S. C. Dass and A. K. Jain, Markov face models. Eighth IEEE International Conference on Computer Vision, pp. 680–687, 2001.

    Google Scholar 

  18. J. Daugman, Face and gesture recognition: overview. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 675–676, 1997.

    Article  Google Scholar 

  19. J. G. Daugman, Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 20, 847–856, 1980.

    Article  PubMed  Google Scholar 

  20. J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortical filters. J. Opt. Soc. Am., 2(7), 1160–1169, 1985.

    Google Scholar 

  21. J. G. Daugman, An information-theoretic view of analog representation in striate cortex. In E. Schwartz (ed.), Computational Neuroscience, pp. 403–424. MIT Press, 1990.

    Google Scholar 

  22. G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman and T. J. Sejnowski, Classifying facial actions. IEEE Trans. Pattern Analysis and Machine Intelligence, 21(10), 974–989, 1999.

    Article  Google Scholar 

  23. S. Edelman and N. Intrator, Learning as extraction of low-dimensional representations. In D. Medin, R. Goldstone and P. Schyns (eds), Mechanisms of Perceptual Learning. Academic Press, 1990.

    Google Scholar 

  24. G. J. Edwards, T. F. Cootes and C. J. Taylor, Face recognition using active appearance models. Proc. Fifth European Conference on Computer Vision, University of Freiburg, Germany, June 2–6, 1998.

    Google Scholar 

  25. K. Etemad and R. Chellappa, Discriminant analysis for recognition of human face images. J. Opt. Soc. Am. A, 14, 1724–1733, 1997.

    Google Scholar 

  26. D. Field, What is the goal of sensory coding. Neural Computation, 6, 559–601, 1994.

    Google Scholar 

  27. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, 1991.

    Google Scholar 

  28. P. Hancock, R. Baddeley and L. Smith, The principal components of natural images. Network, 3, 61–70, 1992.

    Google Scholar 

  29. S. Haykin, Neural Networks — A Comprehensive Foundation. Macmillan College Publishing Company, Inc., 1994.

    Google Scholar 

  30. S. Haykin, Neural Networks — A Comprehensive Foundation, 2nd edn. Prentice Hall, 1999.

    Google Scholar 

  31. B. Heisele, T. Poggio and M. Pontil, Face detection in still gray images. A.I. memo AIM-1687, Artificial Intelligence Laboratory, MIT, 2000.

    Google Scholar 

  32. P. Ho, Rotation invariant real-time face detection and recognition system. A.I. memo AIM-2001-010, Artificial Intelligence Laboratory, MIT, 2001.

    Google Scholar 

  33. R.-L. Hsu, M. Abdel-Mottaleb and A. K. Jain, Face detection in color images. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(5), 696–706, 2002.

    Article  Google Scholar 

  34. R. L. Hsu and A. K. Jain, Face modeling for recognition. International Conference on Image Processing, 2001.

    Google Scholar 

  35. A. Hyvarinen and E. Oja, A fast fixed-point algorithm for independent component analysis. Neural Computation, 9, 1483–1492, 1997.

    Google Scholar 

  36. J. Jones and L. Palmer, An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J. Neurophysiology, 1233–1258, 1987.

    Google Scholar 

  37. J. Karhunen, E. Oja, L. Wang, R. Vigario and J. Joutsensalo, A class of neural networks for independent component analysis. IEEE Trans. Neural Networks, 8(3), 486–504, 1997.

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Wurtz and W. Konen, Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Computers, 42, 300–311, 1993.

    Article  Google Scholar 

  40. A. Lanitis, C. J. Taylor and T. F. Cootes, Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 743–756, 1997.

    Article  Google Scholar 

  41. C. Liu, A Bayesian discriminating features method for face detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 25(6), 725–740, 2003.

    Article  Google Scholar 

  42. C. Liu and H. Wechsler, Comparative assessment of independent component analysis (ICA) for face recognition. In Proc. Second International Conference on Audio-and Video-based Biometric Person Authentication, Washington DC, March 22–24, 1999.

    Google Scholar 

  43. C. Liu and H. Wechsler, Evolutionary pursuit and its application to face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(6), 570–582, 2000.

    Article  Google Scholar 

  44. C. Liu and H. Wechsler, Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. on Image Processing, 9(1), 132–137, 2000.

    Article  Google Scholar 

  45. C. Liu and H. Wechsler, A shape and texture based enhanced Fisher classifier for face recognition. IEEE Trans. on Image Processing, 10(4), 598–608, 2001.

    Article  Google Scholar 

  46. C. Liu and H. Wechsler, Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans. on Image Processing, 11(4), 467–476, 2002.

    Article  Google Scholar 

  47. C. Liu and H. Wechsler, Independent component analysis of Gabor features for face recognition. IEEE Trans. on Neural Networks, 14(4), 2003.

    Google Scholar 

  48. M. J. Lyons, J. Budynek, A. Plante and S. Akamatsu, Classifying facial attributes using a 2D Gabor wavelet representation and discriminant analysis. Proc. Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000.

    Google Scholar 

  49. M. J. Lyons, J. Budynek and S. Akamatsu, Automatic classification of single facial images. IEEE Trans. Pattern Analysis and Machine Intelligence, 21(12), 1357–1362, 1999.

    Article  Google Scholar 

  50. S. Marcelja, Mathematical description of the responses of simple cortical cells. J. Opt. Soc. Am., 70, 1297–1300, 1980.

    PubMed  Google Scholar 

  51. S. Mika, G. Ratsch, J. Weston, B. Scholkopf and K. R. Muller, Fisher discriminant analysis with kernels. In Y. H. Hu, J. Larsen, E. Wilson and S. Douglas (eds), Neural Networks for Signal Processing IX, pp. 41–48. IEEE, 1999.

    Google Scholar 

  52. B. Moghaddam, C. Nastar and A. Pentland, A Bayesian similarity measure for direct image matching. Proc. 13th International Conference on Pattern Recognition, Vienna, Austria, August, 1996.

    Google Scholar 

  53. B. Moghaddam and A. Pentland, Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 696–710, 1997.

    Article  Google Scholar 

  54. A. Mohan, C. Papageorgiou and T. Poggio, Example-based object detection in images by components. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(4), 349–361, 2001.

    Article  Google Scholar 

  55. B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(13), 607–609, 1996.

    Google Scholar 

  56. B. A. Olshausen and D. J. Field, Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Research, 37, 3311–3325, 1997.

    Article  PubMed  Google Scholar 

  57. S. Pankanti, R.M. Bolle and A. Jain, Guest editors’ introduction: Biometrics — the future of identification. Computer, 33(2), 46–49, 2000.

    Google Scholar 

  58. A. Pentland, Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(1), 107–119, 2000.

    Article  Google Scholar 

  59. A. Pentland and T. Choudhury, Face recognition for smart environments. Computer, 33(2), 50–55, 2000.

    Article  Google Scholar 

  60. A. Pentland, B. Moghaddam and T. Starner, View-based and modular eigenspaces for face recognition. Proc. Computer Vision and Pattern Recognition, 1994.

    Google Scholar 

  61. P. J. Phillips, Matching pursuit filters applied to face identification. IEEE Trans. Image Processing, 7(8), 1150–1164, 1998.

    Article  Google Scholar 

  62. P. J. Phillips, Support vector machines applied to face recognition. In Neural Information Processing Systems, 1998.

    Google Scholar 

  63. P. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi and J. M. Bone, FRVT 2002: Evaluation report. Technical report, March, 2003. Available online at http://www.frvt.org/.

    Google Scholar 

  64. P. J. Phillips, A. Martin, C. l. Wilson and M. Przybocki, An introduction to evaluating biometric systems. Computer, 33(2), 56–63, 2000.

    Article  Google Scholar 

  65. T. Poggio, V. Torre and C. Koch, Computational vision and regularization theory. Nature, 317, 314–319, 1985.

    Article  PubMed  Google Scholar 

  66. R. J. Qian and T. S. Huang, Object detection using hierarchical MRF and map estimation. Proc. Computer Vision and Pattern Recognition, 1997.

    Google Scholar 

  67. H. A. Rowley, S. Baluja and T. Kanade, Neural network-based face detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(1), 23–38, 1998.

    Article  Google Scholar 

  68. H. A. Rowley, S. Baluja and T. Kanade, Rotation invariant neural network-based face detection. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, June 23–25, 1998.

    Google Scholar 

  69. A. Samal and P. A. Iyengar, Automatic recognition and analysis of human faces and facial expression: a survey. Pattern Recognition, 25(1), 65–77, 1992.

    Article  Google Scholar 

  70. H. Scheiderman and T. Kanade, Probabilistic modeling of local appearance and spatial relationships for object recognition. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, June 23–25, 1998.

    Google Scholar 

  71. H. Scheiderman and T. Kanade, A statistical method for 3D object detection applied to faces and cars. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000.

    Google Scholar 

  72. B. Scholkopf, A. Smola and K. Muller, Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299–1319, 1998.

    Google Scholar 

  73. K. K. Sung, Learning and example selection for object and pattern detection. PhD Thesis, AI Lab, MIT, 1996.

    Google Scholar 

  74. K. K. Sung and T. Poggio, Example-based learning for view-based human face detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(1), 39–51, 1998.

    Article  Google Scholar 

  75. D. L. Swets and J. Weng, Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(8), 831–836, 1996.

    Article  Google Scholar 

  76. M. Turk and A. Pentland, Eigenfaces for recognition. J. Cognitive Neuroscience, 13(1), 71–86, 1991.

    Google Scholar 

  77. D. Valentin, H. Abdi, A. J. O’Toole and G. W. Cottrell, Connectionist models of face processing: a survey. Pattern Recognition, 27(9), 1209–1230, 1994.

    Article  Google Scholar 

  78. T. Vetter and T. Poggio, Linear object classes and image synthesis from a single example image. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 733–742, 1997.

    Article  Google Scholar 

  79. H. Wechsler, P. J. Phillips, V. Bruce, F. F. Soulie and T. S. Huang (eds), Face Recognition: From Theory to Applications. Springer-Verlag, 1998.

    Google Scholar 

  80. L. Wiskott, J. M. Fellous, N. Kruger and C. von der Malsburg, Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.

    Article  Google Scholar 

  81. M. H. Yang, Kernel eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods. Proc. Fifth International Conference on Automatic Face and Gesture Recognition, Washington DC, May, 2002.

    Google Scholar 

  82. A. L. Yuille, Deformable templates for face recognition. J. Cognitive Neuroscience, 3(1), 59–70, 1991.

    Google Scholar 

  83. W. Zhao and R. Chellappa, 3D model enhanced face recognition. International Conference on Image Processing, 2000.

    Google Scholar 

  84. W. Zhao and R. Chellappa, Symmetric shape-from-shading using self-ratio image. Int. J. Computer Vision, 45(1), 55–75, 2001.

    Article  Google Scholar 

Download references

Authors
  1. Chengjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry Wechsler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. San Jose State University, USA

    James Wayman

  2. Michigan State University, USA

    Anil Jain

  3. University of Bologna, Italy

    Davide Maltoni  & Dario Maio  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag London Limited

About this chapter

Cite this chapter

Liu, C., Wechsler, H. (2005). Face Recognition. In: Wayman, J., Jain, A., Maltoni, D., Maio, D. (eds) Biometric Systems. Springer, London. https://doi.org/10.1007/1-84628-064-8_4

Download citation

  • DOI: https://doi.org/10.1007/1-84628-064-8_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-596-0

  • Online ISBN: 978-1-84628-064-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation