To achieve an abundant texture

a new weighted local binary pattern (W-LBP) is proposed in this study. An image is divided into several sub-images. W-LBP texture histograms are extracted from each sub-image. The proposed algorithm adaptively weights the W-LBP histograms of sub-patches on the basis of their information entropy and serially connects all histograms to create a final texture feature. The process of extracting the W-LBP texture of each sub-image is as follows. First

a local neighborhood is constructed with a certain centered pixel and surrounding eight pixels. A string of binary codes with six bits is then obtained by comparison of three pairs of horizontal and vertical pixels within the local neighborhood with an adaptive threshold. The texture feature of the local region is the sum obtained from the addition of each bit weighted by some weighting factor. Therefore

after the entire sub-image is traversed

the corresponding feature value of each pixel can be calculated. All the values can generate a statistical histogram

which is regarded as the feature value of the sub-image. Experimental results from the two famous face databases indicate that the proposed method with the nearest neighbor classification obtains correct recognition rates of 85.29% and 96.50%. In terms of obtaining abundant texture information

the proposed feature in this study can lead to a high face recognition rate. The new feature also has some reference values for object recognition in other fields.