Local Binary Pattern (LBP) is a theoretically simple yet highly efficient texture descriptor. LBP has recently attracted increasing attention and has been successfully applied in image analysis

computer vision

and pattern recognition because of its discriminative power and computational simplicity. LBP has been developed for the traditional pattern recognition problems of texture classification and face recognition. Considering the theoretical and practical values of LBP

this study comprehensively reviews the suitability of various LBP variants in texture classification and face recognition. The fundamentals of the traditional LBP and various LBP variants are reviewed

and the advantages and disadvantages of various LBP variants are discussed by dividing them into categories under a novel systematic framework. Implicit directions for future LBP studies are also presented. First

the fundamentals of the traditional LBP method and various LBP variants are reviewed in detail and the pros and cons of various LBP variants are discussed by dividing them into different categories under a novel systematic framework. Second

as two typical and most successful applications of the LBP approach

LBP-based texture classification and LBP-based face recognition are reviewed. Finally

the implicit directions for future LBP research are presented. LBP method continues to be a hot research topic in the field of computer vision and pattern recognition

which is evidenced by the fact that new low storage and fast local binary descriptors and new applications of LBP are still emerging.