Abstract
This paper discusses Gaussian decomposition of facial images for robust recognition. While it cannot sufficiently extract an effective component, it can decompose an image into two effective components, the filtered image and its residual. The Gaussian component represents rough information for a lighting condition and small individuality. The residual represents individuality and the other information including small noise. The two components complement each other and they are evaluated independently in the framework of eigenface method. The image decomposition can also collaborate with parallel partial projections for robust recognition.
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Sakaue, F., Shakunaga, T. (2006). Gaussian Decomposition for Robust Face Recognition. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_12
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DOI: https://doi.org/10.1007/11612032_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
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