Abstract
Face representation is one of the open problems in face detection. The recently proposed Multi-Block Local Binary Patterns (MB-LBP) representation has shown good results for this purpose. Although dissimilarity representation has proved to be effective in a variety of pattern recognition problems, to the best of our knowledge, it has never been used for face detection. In this paper, we propose new dissimilarity representations based on MB-LBP features for this task. Different experiments conducted on a public database, showed that the proposed representations are more discriminative than the original MB-LBP representation when classifying faces. Using the dissimilarity representations, a good classification accuracy is achieved even when less training data is available.
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Martínez-Díaz, Y., Méndez-Vázquez, H., Plasencia-Calaña, Y., García-Reyes, E.B. (2012). Dissimilarity Representations Based on Multi-Block LBP for Face Detection. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_13
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DOI: https://doi.org/10.1007/978-3-642-33275-3_13
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