关键词: 局部二元模式（LBP）, 融合隶属度, 指纹分类, K均值

Abstract: In order to improve the speed and the accuracy of fingerprint recognition, especially in the fingerprint database, the scale of which is increasing, fingerprint classification is very important. This paper presents a classification method based on the fusion of membership, and divides the fingerprint into five categories：whorl, arch, tended arch, reverse and reverse loop. This method takes full advantage of the fingerprint texture, adds up statistics by block, fuses the weighted membership, and highlights areas that can distinguish the type of fingerprints to improve the accuracy of classification. Experiments verify that the method has good classification performance.

Key words: Local Binary Pattern（LBP）, fusion membership, fingerprint classification, K-means