Abstract
In previous works we have proposed Lattice Independent Component Analysis (LICA) for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. The set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. In this paper we propose to use the resulting abundance values as features for clasification, specifically for face recognition. We report results on two well known benchmark databases.
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Marqués, I., Graña, M. (2011). Experiments on Lattice Independent Component Analysis for Face Recognition. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_31
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DOI: https://doi.org/10.1007/978-3-642-21326-7_31
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