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Fingerprint matching is an important issue in automatic fingerprint identification systems. There are difficulties about fingerprint matching based on neighborhood. One is the size of the neighborhood can not be determined readily, the other is the feature in the neighborhood can be affected by the noise. To deal with these problem, we developed a novel algorithm for fingerprint matching based on local structures to efficiently extract neighboring minutiae features. Neighboring features present the information of peripheral minutiae which directly connect with the central minutiae on topology. We use one feature vector to present neighboring features from different samples. The samples considered as the same class can make the proposed algorithm robust to rotation and translation of fingerprint images. The experiments are conducted on FVC2002, and the results illustrate the effectiveness of the proposed algorithm.