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.