Computing the Principal Local Binary Patterns for face recognition using data mining tools

Local Binary Patterns are considered as one of the texture descriptors with better results; they employ a statistical feature extraction by means of the binarization of the neighborhood of every image pixel with a local threshold determined by the central pixel. The idea of using Local Binary Patterns for face description is motivated by the fact that faces can be seen as a composition of micro-patterns which are properly described by this operator and, consequently, it has become a very popular technique in recent years. In this work, we show a method to calculate the most important or Principal Local Binary Patterns for recognizing faces. To do this, the attribute evaluator algorithm of the data mining tool Weka is used. Furthermore, since we assume that each face region has a different influence on the recognition process, we have designed a 9-region mask and obtained a set of optimized weights for this mask by means of the data mining tool RapidMiner. Our proposal was tested with the FERET database and obtained a recognition rate varying between 90% and 94% when using only 9 uniform Principal Local Binary Patterns, for a database of 843 individuals; thus, we have reduced both the dimension of the feature vectors needed for completing the recognition tasks and the processing time required to compare all the faces in the database.

► We show a method to calculate the Principal Local Binary Patterns for face recognition.
► We design a 9-region mask and obtain a set of optimized weights for it.
► Data mining tools are utilized for both processes.
► Feature vector dimensions can be reduced up to 96%.
► Recognition rates range from 90% to 94%.