Fingerprint information extraction by twin tracker border line analysis

• US 5,420,937 A
• Filed: 09/01/1993
• Issued: 05/30/1995
• Est. Priority Date: 09/01/1993
• Status: Expired due to Term

• Current Assignee: The Radiant Group, Inc. (Galileo Parent, Inc.)
• Original Assignee: Phoenix Group, Inc. (Assa Abloy AB)
• Inventors: Davis, Phillip S.
• Primary Examiner(s): Couso, Jose L.
• Application Number:
  US08/115,515
• Time in Patent Office: 636 Days
• Field of Search: 382/1, 382/2, 382/3, 382/4, 382/5, 382/48, 382/30, 382/34, 356/71
• US Class Current: 382/125
• CPC Class Codes: G06V 40/1353 Extracting features related to minutiae or pores

Abstract

A method for minutia extraction from a digitized fingerprint array by twin tracker border line analysis includes scanning the array to detect ridge pixels, setting up a pair of tracker pixels or trackers on opposite borders of the ridge, moving the trackers along the borders of the ridge, periodically calculating the midpoint between the trackers to generate a centerline between the borders, calculating an average slope of the centerline, and periodically testing between the trackers for the presence of valley pixels. When such a valley pixel is detected, a sequence of aberration tests is conducted to identify the type of aberration. If the aberration tests all fail, the aberration is identified as a ridge diversion, the location and slope of which are stored as a minutia in a minutia location table. Ridge endings are detected by comparing the coordinates of the trackers after each move. If a ridge ending is detected, an attempt is made to jump to another ridge within a given proximity to the current ridge. When all the ridges have been processed, the valleys of the fingerprint are processed in a similar manner to locate valley minutiae, the locations and slopes of which are stored in the minutia location table associated with the fingerprint.

Claims

1. A method for extracting identification data identifying a selected minutia feature from a plurality of different minutia or aberration types of a generally elongated image element of an image from a rectangular image array of pixels representing said image, and comprising the steps of:

(a) scanning said image array of pixels by a digital processor to detect border pixels defining a border of said elongated element;

(b) determining rectangular border coordinates of said border pixels by using said processor to determine the X-Y coordinates of the detected border pixels;

(c) calculating rectangular centerline coordinates of an average centerline of said elongated element from said border coordinates by using said processor to determine the X-Y coordinates of pixels centered between opposite border pixels of said image element;

(d) detecting the presence of a specific selected minutia or aberration of said elongated element from said border coordinates by using said processor to test said border coordinates to detect certain properties characteristic of each of said plurality of different minutia or aberration types;

(e) using said processor to determine X-Y coordinates of the detected minutia of said element from said border coordinates; and

(f) storing said minutia X-Y coordinates in a digital storage medium as identification data of said detected minutia feature of said element of said image.

2. A method as set forth in claim 1 and including the steps of:

(a) using said processor to calculate a running average slope of said centerline from said centerline coordinates; and

(b) storing an average slope of said centerline adjacent said detected minutia in said digital storage medium as additional identification data of said detected minutia of said element of said image.

3. A method for extracting identification data identifying a selected minutia feature from a plurality of different minutia or aberration types of a generally elongated image element of an image from a rectangular image array of pixels representing said image, and comprising the steps of:

(a) scanning said image array of pixels with a digital processor to detect border pixels defining a border of said elongated element;

(b) determining rectangular border coordinates of said border pixels by using said processor to determine the X-Y coordinates of the detected border pixels;

(c) calculating rectangular centerline coordinates of an average centerline of said elongated element from said border coordinates by using said processor to determine the X-Y coordinates of pixels centered between opposite border pixels of said image element;

(d) using said processor to calculate a running average slope of said centerline from said centerline X-Y coordinates;

(e) detecting the presence of said selected minutia or aberration feature of said element from said border coordinates by using said processor to test said border coordinates to detect certain properties characteristic of each of said plurality of different minutia or aberration types;

(f) using said processor to determine X-Y coordinates of the detected minutia feature of said element from said border coordinates; and

(g) storing in a digital storage medium, as identification data of said minutia feature, said minutia X-Y coordinates and an average slope of said centerline adjacent said detected minutia feature of said element of said image.

4. A method as set forth in claim 3 wherein said image element includes more than one minutia feature, and including the step of:

(a) using said processor to perform said steps of detecting border pixels, determining rectangular border coordinates, calculating rectangular centerline coordinates, calculating a running average slope, detecting the presence and rectangular feature coordinates, and storing feature coordinates and an average slope in said digital storage medium for each minutia feature of said image element.

5. A method as set forth in claim 4 wherein said image includes a plurality of generally elongated image elements, and including the step of:

(a) using said processor to perform said steps of detecting border pixels, determining rectangular border coordinates, calculating rectangular centerline coordinates, calculating a running average slope, detecting the presence and rectangular feature coordinates, and storing feature coordinates and an average slope in said digital storage medium for at least one minutia feature of a plurality of selected image elements of said image.

6. A method as set forth in claim 5 and including the steps of:

(a) using said processor to divide said image array into a grid of image blocks, each said block including a plurality of pixels of said image in each of two rectangular dimensions;

(b) using said processor to define a slope array consisting of a composite average slope corresponding to each of said image blocks; and

(c) using said processor to calculate a composite average slope corresponding to each image block from the average slope of the centerline of the portion of each minutia feature which is positioned within said image block.

7. A method as set forth in claim 4 wherein said image is a digitized representation of an image of a fingerprint, said image elements are representations of ridges of said fingerprint, and said minutia features of said elements are minutiae of said ridges, and including the step of:

(a) using said processor to perform said steps of detecting border pixels, determining rectangular border coordinates, calculating rectangular centerline coordinates, calculating a running average slope, detecting the presence of minutia features and determining X-Y minutia feature coordinates and an average slope for at least one minutia of selected ridges of said image of said fingerprint and storing said X-Y coordinates and said average slope for each detected minutia in said digital storage medium.

8. A method as set forth in claim 4 wherein said image is a digitized representation of an image of a fingerprint, said image elements are representations of valleys between ridges of said fingerprint, and said minutia features of said elements are minutiae of said valleys, and including the step of:

(a) using said processor to perform said steps of detecting border pixels, determining rectangular border coordinates, calculating rectangular centerline coordinates, calculating a running average slope, detecting the presence of and determining X-Y minutia feature coordinates and an average slope for at least one minutia of selected valleys of said image of said fingerprint, and storing said X-Y coordinates and said average slope for each detected minutia in said digital storage medium.

9. A method as set forth in claim 4 wherein said image is a digitized representation of an image of a fingerprint, said image elements are representations of ridges and valleys between ridges of said fingerprint, and said minutia features of said elements are minutiae of said ridges and valleys, and including the step of:

(a) using said processor to perform said steps of detecting border pixels, determining rectangular border coordinates, calculating rectangular centerline coordinates, calculating a running average slope, detecting the presence of, the X-Y coordinates of at least one minutia and the average slope for at least one minutia, and storing said X-Y coordinates and said average slope for each detected minutia of selected ridges and valleys of said image of said fingerprint.

10. A method for extracting information identifying a fingerprint formed of a pattern of ridges separated by valleys and comprising the steps of:

(a) scanning a fingerprint via a fingerprint scanner;

(b) using a frame grabber or similar device to generate a digital image array of data units associated with pixels forming an image of said fingerprint, as said fingerprint is scanned;

(c) using a digital processor to scan said digital image array to locate opposite border lines of ridges of said fingerprint by analysis of said data units associated with said pixels;

(d) generating, via said processor, a respective centerline between opposite border lines of each ridge;

(e) calculating, via said processor, a running average slope of said centerline;

(f) locating at least one minutia or aberration of selected ridges by analysis of said opposite border lines of said ridges, by using said processor to test said border lines to detect certain properties characteristic of each of a plurality of different minutia or aberration types; and

(g) storing, in a digital storage medium, information identifying said fingerprint, said stored information including the location of said minutia of said selected ridges in association with an average slope of the centerline of each selected ridge adjacent said minutia.

11. A method as set forth in claim 10 wherein the minutiae of said selected ridges include divergences of said ridges and wherein said identifying said fingerprint step includes the steps of:

(a) defining a minutia table within said storage medium which is associated with said fingerprint; and

(b) using said processor to store, in said minutia table, locations of divergences of said selected ridges.

12. A method as set forth in claim 10 wherein said step of locating at least one minutia of selected ridges includes the step of:

(a) using said processor to select only ridges which are longer than a selected minimum length for storage of said locations and average centerline slopes.

13. A method as set forth in claim 10 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, and including the step of:

(a) using said processor to locate said opposite border lines by detecting said ridge binary value of ridge border pixels of said ridges which are immediately adjacent pixels along said border lines which have said valley binary value.

14. A method as set forth in claim 13 and including the step of:

(a) upon detecting said ridge binary value of said ridge border pixels, using said processor to change said ridge border pixels to a ridge border binary value.

15. A method as set forth in claim 10 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, and said opposite border line locating step includes the steps of:

(a) using said processor to sequentially read the binary values of said pixels of said array in a selected direction of said array;

(b) using said processor to detect a transition from said valley binary value to said ridge binary value at a first border of a ridge, and to designate as a first border tracker pixel the first pixel detected with said ridge binary value;

(c) using said processor to continue to read through said array in said selected direction until detecting a transition from said ridge binary value to said valley binary value at a second border of said ridge;

(d) designating, via said processor, as a second border tracker pixel the last pixel with said ridge binary value before detecting a pixel with said valley binary value;

(e) using said processor to vary the coordinates of said second tracker pixel by using a border outlining algorithm to move said second tracker to a minimum distance along said second border from said first tracker pixel; and

(f) using said processor to alternately move said first and second tracker pixels respectively along said first and second borders in the same general direction relative to said array to locate all ridge border pixels along said first and second borders, using said border outlining algorithm.

16. A method as set forth in claim 10 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, and said opposite border line locating step includes the steps of:

(a) using said processor to sequentially read the binary values of said pixels of said array in a selected direction of said array;

(b) using said processor to detect a transition from said valley binary value to said ridge binary value at a first border of a ridge, and to designate as a first border tracker pixel the first pixel detected with said ridge binary value;

(c) using said processor to continue to read through said array in said selected direction until detecting a transition from said ridge binary value to said valley binary value at a second border of said ridge;

(d) using said processor to designate as a second border tracker pixel the last pixel with said ridge binary value before detecting a pixel with said valley binary value;

(e) varying, via said processor, the coordinates of said second tracker pixel by using a border outlining algorithm to move said second tracker to a minimum distance along said second border from said first tracker pixel;

(f) using said processor to alternately move said first and second tracker pixels respectively along said first and second borders in the same general direction relative to said array to locate all ridge border pixels along said first and second borders, using said border outlining algorithm; and

(g) using said processor to move said first and second tracker pixels in such an alternating movement cycle as to maintain said first and second tracker pixels respectively at said first and second borders and at substantially a minimum separation distance between said first and second tracker pixels.

17. A method as set forth in claim 16, wherein said generating step includes the steps of:

(a) after each movement cycle, calculating, via said processor, a current midpoint between said first and second tracker pixels; and

(b) generating, via said processor, said centerline between said first and second borders by interconnecting adjacent midpoints of said ridge.

18. A method as set forth in claim 17 wherein said calculating step includes:

(a) selecting, via said processor, only the most recently calculated midpoints for calculation of said centerline running average slope.

19. A method as set forth in claim 16 and including the step of:

(a) after each move of said first or second tracker pixel, comparing, via said processor, the coordinates of said tracker pixels to detect a possible ridge ending if the respective coordinates of said first and second tracker pixels are equal.

20. A method as set forth in claim 16 and including the step of:

(a) upon detecting said possible ridge ending, using said processor to move said first tracker pixel a selected number of moves along said first border to detect further ridge border pixels having said ridge binary value;

(b) upon detecting said further ridge border pixels, using said processor to continue to alternately move said first and second tracker pixels to locate further ridge border pixels; and

(c) upon failing to detect said further ridge border pixels, designating, via said processor, a ridge end pixel of said ridge at a ridge border pixel at which the coordinates of said first and second tracker pixels are detected to be equal.

21. A method as set forth in claim 20 and including the steps of:

(a) upon designating a ridge end pixel, examining, via said processor, pixels within a selected proximity of said end pixel for the presence of a proximate ridge within said selected proximity to the current ridge; and

(b) upon detecting said proximate ridge, using said processor to connect said proximate ridge to said current ridge while continuing to alternately move said first and second tracker pixels to locate further ridge border pixels.

22. A method as set forth in claim 16 and including the steps of:

(a) after each movement cycle, designating, via said processor, an aberration test line of pixels between said first and second tracker pixels;

(b) testing, via said processor, each pixel of said aberration test line for the presence of an aberration pixel having said valley binary value;

(c) upon detecting said aberration pixel, using said processor to conduct a stop sequence of aberration tests in which the sequence of aberration tests is terminated upon one of the aberration tests succeeding in identifying a type of ridge aberration associated with said aberration pixel as a ridge pore, a ridge spur, or a ridge convergence; and

(d) upon all of said aberration tests failing, identifying, via said processor, a ridge divergence minutia at said aberration pixel.

23. A method as set forth in claim 22 in which one of said ridge aberration tests is a ridge pore test, including the steps of:

(a) designating, via said processor, a pore tracker pixel at coordinates of said aberration pixel;

(b) moving, via said processor, said pore tracker pixel to track a border contiguous with said aberration pixel using said border outlining algorithm;

(c) after each move of said pore tracker pixel, comparing, via said processor, current coordinates of said pore tracker pixel with said coordinates of said aberration pixel; and

(d) upon said coordinates of said pore tracker pixel equaling said coordinates of said aberration pixel within a selected number of moves of said pore tracker pixel, using said processor to identify said ridge aberration as a ridge pore.

24. A method as set forth in claim 22 in which one of said ridge aberration tests is a ridge spur test to identify a ridge spur along one of said first and second borders of said ridge, said ridge spur test including the steps of:

(a) designating, via said processor, a spur tracker pixel at coordinates of the one of said first and second tracker pixels which is associated with the one of said first and second borders which is being tested for a spur;

(b) designating, via said processor, a spur test end pixel at the first ridge pixel along said aberration test line on the opposite side of said aberration pixel from said one of said tracker pixels;

(c) using said processor to move said spur tracker pixel along said one of said borders using said border outlining algorithm from said one of said tracker pixels toward said spur test end pixel;

(d) after each move of said spur tracker pixel, comparing, via said processor, current coordinates of said spur tracker pixel with coordinates of said spur test end pixel; and

(e) upon said coordinates of said spur tracker pixel equaling said coordinates of said spur test end pixel within a selected number of moves of said spur tracker pixel, using said processor to identify said ridge aberration as a ridge spur.

25. A method as set forth in claim 22 in which one of said ridge aberration tests is a convergence test to identify a ridge convergence along one of said first and second borders of said ridge including the steps of:

(a) designating, via said processor, a convergence test end pixel at the first ridge pixel along said aberration test line on the opposite side of said aberration pixel from the one of said tracker pixels which is associated with the one of said first and second borders which is being tested for a convergence;

(b) designating, via said processor, a convergence tracker pixel at coordinates of said one of tracker pixels;

(c) using said processor to move said convergence tracker pixel along said one of said borders using said border outlining algorithm from said one of said tracker pixels toward said convergence test end pixel;

(d) after each move of said convergence tracker pixel, comparing, via said processor, current coordinates of said convergence tracker pixel with coordinates of said convergence test end pixel; and

(e) upon said coordinates of said convergence tracker pixel equaling said coordinates of said convergence test end pixel within a selected number of moves of said convergence tracker pixel, using said processor to identify said ridge aberration as a ridge convergence.

26. A method as set forth in claim 22 wherein said ridge divergence has a top fork and a bottom fork and, upon detecting said ridge divergence, said processor:

(a) locates all ridge border pixels along an upper border and a lower border of one of said forks; and

(b) locates all ridge border pixels along an upper border and a lower border of the other of forks.

27. A method as set forth in claim 22 and including the steps of:

(a) after each movement cycle, calculating, via said processor, a current midpoint between said first and second tracker pixels; and

(b) upon detecting said ridge divergence, calculating, via said processor, a ridge divergence slope of said ridge divergence from a selected number of the most previously calculated midpoints.

28. A method as set forth in claim 27 and including the steps of:

(a) defining, in said storage medium, a minutia location table associated with said fingerprint;

(b) upon detecting a ridge divergence, using said processor to store, in said minutia location table, the coordinates of said aberration pixel and said divergence slope to identify the detected ridge divergence as a minutia of said fingerprint.

29. A method as set forth in claim 10 and including the steps of:

(a) using said processor to locate opposite border lines of valleys of said fingerprint by analysis of said data units associated with said pixels;

(b) generating, via said processor, a respective valley centerline between opposite border lines of each valley;

(c) using said processor to calculate a running average slope of said valley centerline;

(d) locating at least one minutia or aberration of selected valleys by analysis of said opposite border lines of said valleys by using said processor to test said border lines to detect certain properties characteristic of each of a plurality of different minutia or aberration types; and

(e) storing, in said digital storage medium, further information identifying said fingerprint, said further information including the location of said minutia of said selected valleys in association with an average slope of the valley centerline of each selected valley adjacent said minutia.

30. A method as set forth in claim 29 wherein said step of locating at least one minutia of selected valleys includes the step of:

(a) using said processor to select only valleys which are longer than a selected minimum length for storage of said locations and average centerline slopes.

31. A method as set forth in claim 29 wherein the minutiae of said selected valleys include divergences of said valleys and said storing step includes:

(a) using said processor to store in said digital storage medium locations of divergences of said selected valleys.

32. A method as set forth in claim 29 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, wherein said valley border detecting step includes:

(a) using said processor to detect said valley binary value of valley border pixels of said valleys which are immediately adjacent pixels of said border lines which have said ridge binary value.

33. A method as set forth in claim 32 and including the step of:

(a) upon detecting said valley binary value of said valley border pixels, using said processor to change said valley border pixels to a valley border binary value.

34. A method as set forth in claim 29 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, and including the step of detecting valley border pixels of a valley which includes the steps of:

(a) using said processor to sequentially read the binary values of said pixels of said array in a selected direction of said array;

(b) using said processor to detect a transition from said ridge binary value to said valley binary value at a first border of a valley, and to designate as a first border tracker pixel the first pixel detected with said valley binary value;

(c) using said processor to continue to read through said array in said selected direction until detecting a transition from said valley binary value to said ridge binary value at a second border of said valley;

(d) designating, via said processor, as a second border tracker pixel the last pixel with said valley binary value before detecting a pixel with said ridge binary value;

(e) using said processor to vary the coordinates of said second tracker pixel by using a border outlining algorithm to move said second tracker to a minimum distance along said second border from said first tracker pixel; and

(f) using said processor to alternately move said first and second tracker pixels respectively along said first and second borders in the same general direction relative to said array to locate all valley border pixels along said first and second borders by using said border outlining algorithm.

35. A method as set forth in claim 29 wherein each pixel of said image array has associated therewith a ridge binary value if said pixel forms a portion of a ridge and a valley binary value if said pixel forms a portion of a valley, and including the step of detecting valley border pixels of a valley which includes the steps of:

(a) using said processor to sequentially read the binary values of said pixels of said array in a selected direction of said array;

(b) using said processor to detect a transition from said ridge binary value to said valley binary value at a first border of a valley, and to designate as a first border tracker pixel the first pixel detected with said valley binary value;

(c) using said processor to continue to read through said array in said selected direction until detecting a transition from said valley binary value to said ridge binary value at a second border of said valley;

(d) using said processor to designate as a second border tracker pixel the last pixel with said valley binary value before detecting a pixel with said ridge binary value;

(e) varying, via said processor, the coordinates of said second tracker pixel by using a border outlining algorithm to move said second tracker a minimum distance along said second border from said first tracker pixel;

(f) using said processor to alternately move said first and second tracker pixels respectively along said first and second borders in the same general direction relative to said array to locate all valley border pixels along said first and second borders, using said border outlining algorithm; and

(g) using said processor to move said first and second tracker pixels in such an alternating movement cycle as to maintain said first and second tracker pixels respectively at said first and second borders and at substantially a minimum separation distance between said first and second tracker pixels.

36. A method as set forth in claim 35 and including the steps of:

(a) after each movement cycle, calculating, via said processor, a current midpoint between said first and second tracker pixels; and

(b) generating, via said processor, said centerline between said first and second borders by interconnecting adjacent midpoints of said valley.

37. A method as set forth in claim 36 and wherein said valley running average slope calculating step includes:

(a) selecting, via said processor, only the most recently calculated midpoints for calculation of said centerline running average slope.

38. A method as set forth in claim 35 and including the step of:

(a) after each move of said first or second tracker pixel, comparing, via said processor, the coordinates of said tracker pixels to detect a possible valley ending if the respective coordinates of said first and second tracker pixels are equal.

39. A method as set forth in claim 38 and including the step of:

(a) upon detecting said possible valley ending, using said processor to move said first tracker pixel a selected number of moves along said first border to detect further valley border pixels having said valley binary value;

(b) upon detecting said further valley border pixels, using said processor to continue to alternately move said first and second tracker pixels to locate further valley border pixels; and

(c) upon failing to detect said further valley border pixels, designating, via said processor, a valley end pixel of said valley at a valley border pixel at which the coordinates of said first and second tracker pixels are detected to be equal.

40. A method as set forth in claim 39 and including the steps of:

(a) upon designating a valley end pixel, examining, via said processor, pixels within a selected proximity of said end pixel for the presence of a proximate valley within said selected proximity to the current valley; and

(b) upon detecting said proximate valley, using said processor to connect said proximate valley to said current valley while continuing to alternately move said first and second tracker pixels to locate further valley border pixels.

41. A method as set forth in claim 35 and including the steps of:

(a) after each movement cycle, designating, via said processor, an aberration test line of pixels between said first and second tracker pixels;

(b) testing, via said processor, each pixel of said aberration test line for the presence of an aberration pixel having said ridge binary value;

(c) upon detecting said aberration pixel, using said processor to conduct a stop sequence of aberration tests in which the sequence of aberration tests is terminated upon one of the aberration tests succeeding in identifying a type of valley aberration associated with said aberration pixel as a valley pore, a valley spur, or a valley convergence; and

(d) upon all of said aberration tests failing, identifying, via said processor, a valley divergence minutia at said aberration pixel.

42. A method as set forth in claim 41 in which one of said valley aberration tests is a pore test, including the steps of:

(a) designating, via said processor, a pore tracker pixel at coordinates of said aberration pixel;

(b) moving, via said processor, said pore tracker pixel to track a border contiguous with said aberration pixel using said border outlining algorithm;

(c) after each move of said pore tracker pixel, comparing, via said processor, current coordinates of said pore tracker pixel with said coordinates of said aberration pixel; and

(d) upon said coordinates of said pore tracker pixel equaling said coordinates of said aberration pixel within a selected number of moves of said pore tracker pixel, using said processor to identify said valley aberration as a valley pore.

43. A method as set forth in claim 41 in which one of said valley aberration tests is a valley spur test to identify a valley spur along one of said first and second borders of said valley, said spur test including the steps of:

(a) designating, via said processor, a spur tracker pixel at coordinates of the one of said first and second tracker pixels which is associated with the one of said first and second borders which is being tested for a spur;

(b) designating, via said processor, a spur test end pixel at the first valley pixel along said aberration test line on the opposite side of said aberration pixel from said one of said tracker pixels;

(c) using said processor to move said spur tracker pixel along said one of said borders using said border outlining algorithm from said one of said tracker pixels toward said spur test end pixel;

(d) after each move of said spur tracker pixel, comparing, via said processor, current coordinates of said spur tracker pixel with coordinates of said spur test end pixel; and

(e) upon said coordinates of said spur tracker pixel equaling said coordinates of said spur test end pixel within a selected number of moves of said spur tracker pixel, using said processor to identify said valley aberration as a valley spur.

44. A method as set forth in claim 41 in which one of said valley aberration tests is a convergence test to identify a valley convergence along one of said first and second borders of said valley, including the steps of:

(a) designating, via said processor, a convergence test end pixel at the first valley pixel along said aberration test line on the opposite side of said aberration pixel from the one of said tracker pixels which is associated with the one of said first and second borders which is being tested for a convergence;

(b) designating, via said processor, a convergence tracker pixel at coordinates of said one of tracker pixels;

(c) using said processor to move said convergence tracker pixel along said one of said borders using said border outlining algorithm from said one of said tracker pixels toward said convergence test end pixel;

(d) after each move of said convergence tracker pixel, comparing, via said processor, current coordinates of said convergence tracker pixel with coordinates of said convergence test end pixel; and

(e) upon said coordinates of said convergence tracker pixel equaling said coordinates of said convergence test end pixel within a selected number of moves of said convergence tracker pixel, using said processor to identify said valley aberration as a valley convergence.

45. A method as set forth in claim 41 wherein said valley divergence has a top fork and a bottom fork and, upon detecting said valley divergence, said processor:

(a) locates all valley border pixels along an upper border and a lower border of one of said forks; and

(b) locates all valley border pixels along an upper border and a lower border of the other of forks.

46. A method as set forth in claim 41 and including the steps of:

(a) after each movement cycle, calculating, via said processor, a current midpoint between said first and second tracker pixels; and

(b) upon detecting said valley divergence, calculating, via said processor, a valley divergence slope of said valley divergence from a selected number of the most previously calculated midpoints.

47. A method as set forth in claim 46 and including the steps of:

(a) defining, in said storage medium, a minutia location table associated with said fingerprint;

(b) upon detecting a valley divergence, using said processor to store, in said minutia location table, the coordinates of said aberration pixel and said divergence slope to identify the detected valley divergence as a minutia of said fingerprint.