Image identification system

A method for comparing a first image data set to a second image data set, wherein the first and second image data sets are derived from fingerprint images and include a plurality of data elements comprising at least one of bifurcation representations, rod representations, vector segments associated with bifurcation representations, vector segments associated with rod representations, vector segments not associated with bifurcation representations, vector segments not associated with rod representations, microminutia points, and combinations thereof, the method comprising:

• re-positioning at least one of the first and second image data sets, thereby re-positioning the set of data elements associated therewith;
• generating a count of the data elements in the first image data set that approximately match the data elements in the second image data set;
• and
• repeating said re-positioning and said generating a count until a maximum comparison point is identified, said maximum comparison point being a point at which said count approximates a maximum value.

A method for efficiently and accurately comparing a first image data set to a plurality of other image data sets, wherein the first and plurality of other image data sets are individually derived from fingerprint images and include a plurality of data elements of a plurality of types, the plurality of types comprising at least one of bifurcation representations, rod representations, vector segments associated with bifurcation representations, vector segments associated with rod representations, vector segments not associated with bifurcation representations, vector segments not associated with rod representations, microminutia points, and combinations thereof, the method comprising:

• creating a directory that progressively lists, based on at least one measured characteristic, a substantial number of a first type of data elements that appear in the plurality of other image data sets, wherein each data element in the directory includes an identifier that represents association with a particular image data set within which the data element appears;
• creating an array having a two-entry cell for each of a range of potential configurations for the first type of data element;
• recording in a first entry of at lest one two-entry cell, a quantity value representing a number of consecutive data elements in the directory that demonstrate characteristics that are approximately similar to characteristics of the one of the range of potential configurations for the first type of data element that is associated with the two-entry cell within which the first entry is being recorded;
• recording in a second entry of at least one two-entry cell, an index value corresponding to an initial data element that begins the number of consecutive data elements listed in the directory;
• identifying a two-entry cell in the array that is associated with a data element configuration having characteristics approximately identical to a target data element of the first type taken from the first image data set;
• and
• comparing the target data element to a group of consecutive data elements listed in the directory, as indicated by the two-entry cell associated with the target data element.

A method for efficiently and accurately comparing a first image data set to a plurality of other image data sets, wherein the first and plurality of other image data sets are individually derived from fingerprint images and include a plurality of data elements of a plurality of types, the plurality of types comprising at least one of bifurcation representations, rod representations, vector segments associated with bifurcation representations, vector segments associated with rod representations, vector segments not associated with bifurcation representations, vector segments not associated with rod representations, microminutia points, and combinations thereof, the method comprising:

• creating a B-tree data file for at least one of the plurality of data element types and categorizing and storing, based on a set of data normalization rules, substantially all of the data elements included in the plurality of other image data sets;
• storing each data element in the B-tree data files with an identifier that represents association with a particular image data set within which the data element appears;
• and
• comparing at least one target data element from the first image data set to data elements in the B-tree data file having data elements of a same type as each target data element being compared.