Current Development in Theory and Applications of Wavelets
Volume 1, Issue 1, Pages 107 - 124
(April 2007)
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WAVELET NETWORKS FOR OBJECT REPRESENTATION AND FACE RECOGNITION
V. Krüger (Denmark)
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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 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 this paper, we analyze some of their properties and hightlight their advantages for object representation purposes. We then present a series of experimental results where we have used WNs for face tracking in which we exploit the efficiency due to data reduction and for face recognition where we exploit the optimized filter bank principle of the WNs. |
| Keywords and phrases: wavelet networks, object representation, face recognition. |
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