In this paper, we present a new distinctive feature for tex- ture classification, the fan-shaped patch local binary pat- terns (FP-LBP). The proposed FP-LBP operator extends the traditional LBP operator by encoding the difference be- tween each central pixel with the average value of its neigh- boring fan-shaped patches, instead of only using its neigh- boring pixels. By this way, FP-LBP not only preserves more information of local structures than the traditional LBP, but also keeps relatively lower dimensionality, espe- cially when larger radius and more neighboring pixels are considered. Moreover, the “uniform” and rotation invari- ant FP-LBP are also defined similarly to the traditional LBP. The proposed descriptors are evaluated on two pop- ular texture databases: CUReT and KTH-TIPS, and the ex- perimental results show that FP-LBP outperforms the tra- ditional LBP descriptor with a smaller feature dimension. Moreover, the proposed method achieves higher classifica- tion accuracy than most of the state-of-the-art methods on both databases.