Abstract Local binary patterns (LBP) achieve great success in texture analysis, however they are not robust to noise. The two reasons for such disadvantage of LBP schemes are (1) they encode the texture spatial structure based only on local information which is sensitive to noise and (2) they use exact values as the quantization thresholds, which make the extracted features sensitive to small changes in the input image. In this paper, we propose a noise-robust adaptive hybrid pattern (AHP) for noised texture analysis. In our scheme, two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature's noise sensitiveness. First, a hybrid texture description model is proposed. In this model, the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description. Second, we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy. Higher noise-tolerance can be obtained with the minimum lost information in the quantization process. The experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis. Furthermore, our scheme achieves state-of-the-art performance in noisy face recognition. © 2015 Elsevier Ltd. All rights reserved.