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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 19, Iss. 6 — Jun. 1, 2002
  • pp: 1112–1119

Wavelet networks for face processing

V. Krüger and G. Sommer  »View Author Affiliations


JOSA A, Vol. 19, Issue 6, pp. 1112-1119 (2002)
http://dx.doi.org/10.1364/JOSAA.19.001112


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Abstract

Wavelet networks (WNs) were introduced in 1992 as a combination of artificial neural radial basis function (RBF) networks and wavelet decomposition. Since then, however, WNs have received only a little attention. We believe that the potential of WNs has been generally underestimated. WNs have the advantage that the wavelet coefficients are directly related to the image data through the wavelet transform. In addition, the parameters of the wavelets in the WNs are subject to optimization, which results in a direct relation between the represented function and the optimized wavelets, leading to considerable data reduction (thus making subsequent algorithms much more efficient) as well as to wavelets that can be used as an optimized filter bank. In our study we analyze some WN properties and highlight their advantages for object representation purposes. We then present a series of results of experiments in which we used WNs for face tracking. We exploit the efficiency that is due to data reduction for face recognition and face-pose estimation by applying the optimized-filter-bank principle of the WNs.

© 2002 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.7410) Image processing : Wavelets

Citation
V. Krüger and G. Sommer, "Wavelet networks for face processing," J. Opt. Soc. Am. A 19, 1112-1119 (2002)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-19-6-1112


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References

  1. Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Trans. Neural Netw. 3, 889–898 (1992).
  2. H. Szu, B. Telfer, and S. Kadambe, “Neural network adaptive wavelets for signal representation and classification,” Opt. Eng. 31, 1907–1961 (1992).
  3. H. Szu, B. Telfer, and J. Garcia, “Wavelet transforms and neural networks for compression and recognition,” Neural Networks 9, 695–708 (1996).
  4. Q. Zhang, “Using wavelet network in nonparametric estimation,” IEEE Trans. Neural Netw. 8, 227–236 (1997).
  5. C. C. Holmes and B. K. Mallick, “Bayesian wavelet networks for nonparametric regression,” IEEE Trans. Neural Netw. 11, 27–35 (2000).
  6. L. Reyneri, “Unification of neural and wavelet networks and fuzzy systems,” IEEE Trans. Neural Netw. 10, 801–814 (1999).
  7. I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans. Inf. Theory 36, 961–1005 (1990).
  8. J. Daugman, “Complete discrete 2D Gabor transform by neural networks for image analysis and compression,” IEEE Trans. Acoust. Speech Signal Process. 36, 1169–1179 (1988).
  9. T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996).
  10. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes. The Art of Scientific Computing (Cambridge U. Press, Cambridge, UK, 1986).
  11. J. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized two-dimensional visual cortical filters,” J. Opt. Soc. Am. A 2, 1160–1168 (1985).
  12. R. Feris, V. Krüger, and R. Cesar, Jr., “Efficient real-time face tracking in wavelet subspace,” in Proceedings of the International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems (IEEE Computer Society, Santa Ana, Calif., 2001), pp. 113–118.
  13. V. Krüger and G. Sommer, “Affine real-time face tracking using gabor wavelet networks” in Proceedings of the International Conference on Pattern Recognition (IEEE Computer Society, Santa Ana, Calif., 2000), pp. 127–130.
  14. A. Pentland, “Looking at people: sensing for ubiquitous and sensable computing,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 107–119 (2000).
  15. V. Krüger, S. Bruns, and G. Sommer. Efficient head pose estimation with gabor wavelet networks, in Proceedings of the British Machine Vision Conference (British Machine Vision Association, Malverne, UK, 2000), pp. 72–81.
  16. J. Bruske and G. Sommer, “Dynamic cell structure learns perfectly topology preserving map,” Neural Comput. 7, 845–865 (1995).
  17. H. Ritter, T. Martinez, and K. Schulten, Neuronale Netze (Addison-Wesley, Reading, Mass., 1991).
  18. V. Krüger, “Gabor wavelet networks for object representation,” Tech. Rep. CS-TR-4245 (Center for Automation Research, University of Maryland, College Park, Md., 2001).
  19. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).

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