Abstract
In this paper, we propose a new scheme of Gabor-based face recognition. Based on the fact that different Gabor filters have different properties, we first learn discriminating subspace for each kind of Gabor images respectively. Then the boosting learning is performed to fuse all the Gabor discriminating subspaces for recognition. Compared with previous work, the proposed method has three contributions: (1). We make sufficiently use of the respective properties of the Gabor filters, and learn different discriminant subspaces for different Gabor images respectively; (2). Boosting based fusing method adaptively determines the discriminating vectors and dimensionality of each subspace according to its discriminating capacity, so as to further improve the recognition performance; (3). The problem of computational complexity is well handled by subspace analysis and boosting based fusion. Extensive experiments show its encouraging performance.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, Q., Jin, H., Tang, X., Lu, H., Ma, S. (2006). Boosting Multi-gabor Subspaces for 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_55
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DOI: https://doi.org/10.1007/11612032_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
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