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.
Cite
CITATION STYLE
Ma, K., & Tang, X. (2001). Translation-invariant face feature estimation using discrete wavelet transform. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2251, pp. 200–210). Springer Verlag. https://doi.org/10.1007/3-540-45333-4_25
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