The representation of writing styles is a crucial step of writer identification schemes. However, the large intra-writer variance makes it a challenging task. Thus, a good feature of writing style plays a key role in writer identification. In this paper, we present a simple and effective feature for off-line, text-independent writer identification, namely wavelet domain local binary patterns (WD-LBP). Based on WD-LBP, a writer identification algorithm is developed. WD-LBP is able to capture the essence of characteristics of writer while ignoring the variations intrinsic to every single writer. Unlike other texture framework method, we do not assign any statistical distribution assumption to the proposed method. This prevent us from making any, possibly erroneous, assumptions about the handwritten image feature distributions. The experimental results show that the proposed writer identification method achieves high accuracy of identification and outperforms recent writer identification method such as wavelet-GGD model and Gabor filtering method.