A new method called weighted Local Binary Pattern (LBP) with adaptive threshold is proposed in this paper to address the shortcomings of LBP and Center Symmetric Local Binary Pattern (CS-LBP), using unflexible threshold and non- discriminating respective sub-patches based on different textures. Firstly, the image is divided into several sub-images and LBP or CS-LBP texture histograms are extracted respectively from each sub-image based on the adaptive threshold. Then, the proposed algorithm adaptively weighted the LBP or CS-LBP histograms of sub-patches with information entropy as their basis and connected all histograms serially to create a final texture descriptor. Finally, the improved efficiency of the proposed algorithm is achieved by speeding up the computation of the average of an image. The experimental results by face databases show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.