Aiming at the problem that traditional local binary pattern operators lack deep-level correlation information between pixels and have poor robustness to common blurring and rotation changes in images, a robust texture expression operator combining differential features and Haar wavelet decomposition was proposed. In the differential feature channel, the first-order and second-order differential features in the image were extracted by the isotropic differential operators, so that the differential features of the image were essentially invariant to rotation and robust to image blur. In the wavelet decomposition feature extraction channel, based on the characteristic that the wavelet transform has good localization in the time domain and frequency domain at the same time, multi-scale two-dimensional Haar wavelet decomposition was used to extract blurring robustness features. Finally, the feature histograms on the two channels were concatenated to construct a texture description of the image. In the feature discrimination experiments, the accuracy of the proposed operator on the complex UMD, UIUC and KTH-TIPS texture databases reaches 98.86%, 98.2% and 99.05%, respectively, and compared with that of the MRELBP (Median Robust Extended Local Binary Pattern) operator, the accuracy increases by 0.26%, 1.32% and 1.12% respectively. In the robustness analysis experiments on rotation change and image blurring, the classification accuracy of the proposed operator on the TC10 texture database with only rotation changes reaches 99.87%, and the classification accuracy decrease of the proposed operator on the TC11 texture database with different levels of Gaussian blurs is only 6%. In the computational complexity experiments, the feature dimension of the proposed operator is only 324, and the average feature extraction time of the proposed operator on the TC10 texture database is 30.9 ms. Experimental results show that the method combining differential feature and Haar wavelet decomposition has strong feature discriminability and strong robustness to rotation and blurring, as well as has low computational complexity. It has good applicability in situations with small database.

Under the influence of illumination, pose, expression, occlusion and noise, the recognition rate of faces is excessively low, therefore a method combining weighted Information Entropy (IEw) with Adaptive-Threshold Ring Local Binary Pattern (ATRLBP) (IEwATR-LBP) was proposed. Firstly, the information entropy was extracted from the sub-blocks of the original face image, and then the IEw of each sub-block was obtained. Secondly, the probability histogram was obtained by using ATRLBP operator to extract the features of face sub-blocks. Finally, the final feature histogram of original face image was obtained by concatenating the multiplications of each IEw with the probability histogram, and the recognition result was calculated through Support Vector Machine (SVM). In the comparison experiments on the illumination, pose, expression and occlusion datasets from AR face database, the proposed method achieved recognition rates of 98.37%, 94.17%, 98.20%, and 99.34% respectively; meanwile, it also achieved the maximum recognition rate of 99.85% on ORL face database. And the average recognition rates in 5 experiments with different training samples were compared to conclude that the recognition rate of samples with Gauss noise was 14.04 percentage points lower than that of samples without noise, while the recognition rate of samples with salt & pepper noise was only 2.95 percentage points lower than that of samples without noise. Experimental results show that the proposed method can effectively improve the recognition rate of faces under the influence of illumination, pose, occlusion, expression and impulse noise.