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. 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. 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%

10.7%

and 0.7%

respectively. At the same time

the double weighted decision fusion pattern proposed in this study obtained the average recognition rates of 98.5% and 98.34% on ORL and Yale database

respectively. These rates are higher than any single module. In this study

a new texture extraction algorithm called ULMP is proposed. The ULMP has a good effect on smoothing noise and edge information and can achieve a high recognition rate. The ULMP is suitable for extracting facial features. At the same time

the method employs the cloud model to derive the weights that can better develop the integration between the local classifiers. Ultimately

the ULMP improves the overall performance of the system and obtains a higher recognition rate. The ULMP is compared with other methods to verify its validity. Results show that the double weighted fusion model is a precise and effective method for face recognition.