To improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition.A novel method is proposed in this paper by com bining blocking center symmetric local binary pattern (CS-LBP) and weighted principal component analysis (PCA).Firstly,the features of low-resolution face images are extracte d by blocking CS-LBP operator.Secondly,the stronger classification and lower dimension features can be got by applying weighted PCA algorithm.Finally the distance is calculated and used to select the optimal clas sification categories of low-r esolution face set by using the nearest neighbor classifier.Besides, the recognition rate also can be calculated.The experimental results in the ORL human face database show that recognition rate can reach 85.00% when resolution of face image drops to (12×10),which can basically s atisfy the practical requirements of recognition and reduce computation time on low-resolution face recognition.