Abstract
In recent years, Local Binary Patterns have proved to be a powerful local descriptor for microstructures of images, having been introduced in many facial recognition systems and intelligent environments. In this work, we present the implementation of a face recognition method based on the use of Local Binary Patterns. We used data mining tools to get a smaller feature vector and thus improve the computational cost of the system. The implementation was tested with the Color FERET database, obtaining a recognition rate of 94% and reducing 75% the original feature vector dimension.
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García, J.C., Pujol, F.A. (2011). Feature Reduction of Local Binary Patterns Applied to Face Recognition. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_32
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DOI: https://doi.org/10.1007/978-3-642-19934-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19933-2
Online ISBN: 978-3-642-19934-9
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