The local directional pattern (LDP) descriptor is a method for texture feature extraction. It calculates and sorts edge response values of eight different directions, thus the speed is slower than other local texture feature extraction algorithm. This paper presents a new feature descriptor called divided local directional pattern (DLDP) for feature extraction. In this method, Kirsch masks in eight different orientations were divided into two sub-directional masks. The edge response values were calculated respectively to obtain DLDP1 and DLDP2. DLDP1 and DLDP2 were concatenated into a single DLDP descriptor. Then principal component analysis (PCA) was used for dimensionality reduction processing. Finally, the support vector machine (SVM) was applied to classify and recognize facial expression. The experimental results show that compared with the better feature extraction algorithms in recent years, the improved local direction pattern can not only reduce the computation time, but also improve the rate of facial expression recognition.