An Extended Local Binary Pattern for Gender Classification

This paper addresses the problem of gender recognition by proposing a new feature descriptor to be used in classification. The contribution of this work is an extension to the local binary patterns traditionally used as descriptors. Local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method we incorporate into the descriptor more information from the neighborhood by using four predefined patterns, rather than just one, as in the classic model. We evaluate the performance of our method on the standard FERET database by comparing it to existing methods and show that we can extract more discriminative features and subsequently provide better gender recognition accuracy.