Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been usedin pattern recognition and computer vision applications. However, the LBP gets information from a restrictednumber of local neighbors which is not enough to describe texture information, and the other descriptors thatget a large number of local neighbors suffer from a large dimensionality and consume much time. In thisregard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern(CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region ofneighbors using polar coordinates that permit to capture more discriminating relationships that exists amongstthe pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle isdivided into regions with a specific radius and angle. After that, we calculate the average gray-level value ofeach part. Finally, the value of the center pixel is compared with these average values. The relevance of theproposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets revealsCPLBP′s high performance. CPLBP generates the score of 99.95 with SVM classification.