Objective The LBP(local binary pattern) algorithm is sensitive to edge and noise. Thus, this study proposes a new algorithm called uniform local mean pattern (ULMP). Considering the complementarity of global and local features on recognition, this study proposes a face recognition method based on ULMP description and double weighted decision fusion for classification. Method First, we use the ULMP algorithm to derive the code diagram of the entire image. The concrete steps to obtain the eight binary codes are implemented by comparing the eight average pixels with the center pixel. Each of the eight values is obtained by computing the average pixels of the eight directions, three horizontal directions, three vertical directions, and two diagonal directions. Each binary code is multiplied by the corresponding weight coefficient and then added to derive the ULMP coding value of the center pixel and the code pattern of the entire image. Then, the code diagram is divided into equal sub-blocks and each sub-block histogram is assessed to determine the local texture features. The global texture feature is obtained by connecting the histogram of different sub-block features. To emphasize the importance of different sub-blocks in the final recognition, this study introduces the cloud model and structure-based classifiers by constructing sub-image sets to obtain the weight of each sub-block. In the testing phase, each block of the statistical characteristics of a test sample is combined with the BP neural network to determine the posterior probability of each category. We use the weights calculated by the cloud model fused with the linear weighted decision to derive the local classification results. After obtaining the results of local and global classification, we conducted weighted integration to obtain the final recognition results. Result The experimental results on the ORL and Yale face database show that the ULMP exhibits good recognition performance. The average recognition rate is 95.9% on the ORL database with five test samples. The proposed approach increases the recognition rate of LBP, MCT(modified census transform), LGP(local gradient patterns), ULBP, and CSLBP by 11.3%, 10.6%, 9.5%, 8.9%, and 3.9%, respectively. The average recognition rate is 97.4% on the Yale database with five test samples. The proposed approach increases the recognition rate of LBP, MCT, LGP, ULBP, and CSLBP by 18.9%, 17.7%, 17.1%, Objective The LBP(local binary pattern) algorithm is sensitive to edge and noise. Thus, this study proposes a new algorithm called uniform local mean pattern (ULMP). Considering the complementarity of global and local features on recognition, this study proposes a face recognition method based on ULMP description and double weighted decision fusion for classification. Method First, we use the ULMP algorithm to derive the code diagram of the entire image. The concrete steps to obtain the eight binary codes are implemented by comparing the eight average pixels with the center pixel. Each of the eight values is obtained by computing the average pixels of the eight directions, three horizontal directions, three vertical directions, and two diagonal directions. Each binary code is multiplied by the corresponding weight coefficient and then added to derive the ULMP coding value of the center pixel and the code pattern of the entire image. Then, the code diagram is divided into equal sub-blocks and each sub-block histogram is assessed to determine the local texture features. The global texture feature is obtained by connecting the histogram of different sub-block features. To emphasize the importance of different sub-blocks in the final recognition, this study introduces the cloud model and structure-based classifiers by constructing sub-image sets to obtain the weight of each sub-block. In the testing phase, each block of the statistical characteristics of a test sample is combined with the BP neural network to determine the posterior probability of each category. We use the weights calculated by the cloud model fused with the linear weighted decision to derive the local classification results. After obtaining the results of local and global classification, we conducted weighted integration to obtain the final recognition results. Result The experimental results on the ORL and Yale face database show that the ULMP exhibits good recognition performance. The average recognition rate is 95.9% on the ORL database with five test samples. The proposed approach increases the recognition rate of LBP, MCT(modified census transform), LGP(local gradient patterns), ULBP, and CSLBP by 11.3%, 10.6%, 9.5%, 8.9%, and 3.9%, respectively. The average recognition rate is 97.4% on the Yale database with five test samples. The proposed approach increases the recognition rate of LBP, MCT, LGP, ULBP, and CSLBP by 18.9%, 17.7%, 17.1%, 

Objective The LBP(local binary pattern) algorithm is sensitive to edge and noise. Thus, this study proposes a new algorithm called uniform local mean pattern (ULMP). Considering the complementarity of global and local features on recognition, this study proposes a face recognition method based on ULMP description and double weighted decision fusion for classification. Method First, we use the ULMP algorithm to derive the code diagram of the entire image. The concrete steps to obtain the eight binary codes are implemented by comparing the eight average pixels with the center pixel. Each of the eight values is obtained by computing the average pixels of the eight directions, three horizontal directions, three vertical directions, and two diagonal directions. Each binary code is multiplied by the corresponding weight coefficient and then added to derive the ULMP coding value of the center pixel and the code pattern of the entire image. Then, the code diagram is divided into equal sub-blocks and each sub-block histogram is assessed to determine the local texture features. The global texture feature is obtained by connecting the histogram of different sub-block features. To emphasize the importance of different sub-blocks in the final recognition, this study introduces the cloud model and structure-based classifiers by constructing sub-image sets to obtain the weight of each sub-block. In the testing phase, each block of the statistical characteristics of a test sample is combined with the BP neural network to determine the posterior probability of each category. We use the weights calculated by the cloud model fused with the linear weighted decision to derive the local classification results. After obtaining the results of local and global classification, we conducted weighted integration to obtain the final recognition results. Result The experimental results on the ORL and Yale face database show that the ULMP exhibits good recognition performance. The average recognition rate is 95.9% on the ORL database with five test samples. The proposed approach increases the recognition rate of LBP, MCT(modified census transform), LGP(local gradient patterns), ULBP, and CSLBP by 11.3%, 10.6%, 9.5%, 8.9%, and 3.9%, respectively. The average recognition rate is 97.4% on the Yale database with five test samples. The proposed approach increases the recognition rate of LBP, MCT, LGP, ULBP, and CSLBP by 18.9%, 17.7%, 17.1%,