Abstract
In this paper, we conduct a series of experiments to demonstrate the translation invariant property of a set of discrete wavelet features in a face graph. Using local-area power spectrum estimation based on discrete wavelet transform, we compute a feature vector that possesses both an efficient space-frequency structure and the translation invariant property.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Antoniadis and G. Oppenheim, Wavelet and statistics, Springer-Verlag, 1995.
K. Chui, An Introduction to Wavelets, Academic Press, Boston, 1992.
Daubechies, Ten Lectures on Wavelets, SIAM Publ., Philadelphia, 1992.
S. Mallat, A Wavelet Tour of Signal Processing, 2nd Ed., Academic Press, 1999.
E. P. Simoncelli, W.T. Freeman, E.H. Adelson, and D.J. Heeger, "Shiftable multiscale transforms", IEEE Trans. on Information Theory, Vol. 38, No. 2, pp. 587–607, Mar. 1992.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, K., Tang, X. (2001). Translation-Invariant Face Feature Estimation Using Discrete Wavelet Transform. In: Tang, Y.Y., Yuen, P.C., Li, Ch., Wickerhauser, V. (eds) Wavelet Analysis and Its Applications. WAA 2001. Lecture Notes in Computer Science, vol 2251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45333-4_25
Download citation
DOI: https://doi.org/10.1007/3-540-45333-4_25
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43034-6
Online ISBN: 978-3-540-45333-8
eBook Packages: Springer Book Archive