指纹检索通过有效地缩小指纹匹配空间，在很大程度上提高了指纹识别的效率，成为自动指纹识别系统中一个非常重要的研究方向。主要做了两方面的工作：发现已有Orientation_based Minutia Descriptor(OMD)存在旋转相关的问题并通过改进OMD相似度计算方法有效地解决了该问题；提出了一种基于改进的OMD指纹检索算法，其主要过程为：依据改进的OMD相似度计算方法计算当前输入指纹与库中指纹每两个细节点间的OMD相似度，并基于该相似度获取粗糙对应点集；对粗糙对应点集中的每对对应点对，通过构造相

Fingerprint indexing heightens the efficiency of identification to a large extent through reducing the matching space effectively, thereby, it becomes a very important research area in Automatic Fingerprint Identification System(AFIS). This paper mainly includes two work: discovering that the Orientation_based Minutia Descriptor(OMD) is rotation-interrelated and solving the problem by improving the OMD similarity-degree computing method; proposing a new fingerprint indexing method based on improved OMD. The main procedure of our fingerprint indexing method is: calculating the OMD similarity-degree between all the point-pairs which from the current input fingerprint and the fingerprint in database respectively by using the improved OMD similarity-degree computing method, and obtaining the coarse-level correspondence set based on the OMD similarity-degree; constructing the local neighborhood structure to validate the coarse level correspondence further, and obtaining the refined correspondence set; obtaining the rest refined correspondence sets between the current input fingerprint and the rest fingerprints in database by repeating the two fore-mentioned steps; counting the number of correspondences for all refined correspondence set, getting the top N refined correspondence sets and returning their corresponding finger labels. Experiment validated the effectiveness of our method.