Matcher

• US 4,618,988 A
• Filed: 07/25/1984
• Issued: 10/21/1986
• Est. Priority Date: 07/25/1984
• Status: Expired due to Fees

• Current Assignee: Secure Technologies Group, Inc.
• Original Assignee: Secure Technologies Group, Inc.
• Inventors: Schiller, Michael
• Primary Examiner(s): Ciarlante, Anthony V.
• Application Number:
  US06/634,410
• Time in Patent Office: 818 Days
• Field of Search: 382/5, 382/36, 382/4
• US Class Current: 382/125
• CPC Class Codes: G06V 40/12 Fingerprints or palmprints

Abstract

In a matcher mechanism for verifying a match of a set of subject points having positional parameters against a set of reference points having positional parameters, the improvement of employing a set of Group tables derived from the difference table. Each line entry on the difference table constitutes a lead entry of a separate Group table. The Group table are overlapping subsets of the entries on the difference table. The members of the Group table are limited to only one line entry representing each reference point. That line entry is the one which is closest to the lead entry on the difference plane. In addition, all line entries within a Group are limited to those that are within a predetermined window on the difference plane around the lead line entry for the group. A quality score is determined for each of the Group tables based on a weighted proximity value between each pair of line entries in the Group involved. The closer a pair of line entries are to each other, the greater will be its weighted score and its contribution to the weighted proximity value for the Group table. That Group table with the greatest quality score is deemed to provide the quality score for the matcher. A match is determined if the value of that quality score exceeds a threshold which is a function of the number of reference identification points. There may also be a requirement that the quality score exceeds some minimum value.

Claims

1. In the method of matching a set of subject identification points against a set of reference identification points involving a matcher mechanism that employs a difference table, the improvement comprising the steps of:

optically scanning the surface of an object that is deemed to be a subject input to provide said set of subject identification points substantially unique to the object being scanned,

for each line entry on the difference table as a lead entry, providing a group of difference table line entries, limiting each group so provided to no more than one line entry for each of said reference identification points,

for each of said groups, providing a quality value determined by a weighted relationship between the relative displacements of the various line entries in the group,

selecting the most significant group quality value, and

verifying a match between the set of subject points and the set of reference points as a function of the relative magnitude of the most significant quality value as compared to the number of identification points in a first one of said sets.

2. The method of claim 1 wherein said step of providing each of said groups involves selecting that one line entry associated with each reference point which is closest on the difference plane to the lead entry for that group.

3. The method of claim 2 wherein each of said groups, all line entries within a group are within a predetermined window on the difference plane around the lead line entry for that group.

4. The method of claim 3 wherein said first one of said sets, used in said step of verifying, is said set of reference identification points.

5. The method of claim 4 wherein said surface of an object is a finger surface or a fingerprint surface.

6. The method of claim 3 wherein said weighted relationship involves determining a weighted proximity value between each pair of line entries in the group involved.

7. The method of claim 6 wherein said weighted proximity value is based on relative displacement on the difference plane of each pair.

8. The method of claim 7 wherein said weighted proximity value if further based on the relative angular displacement of each pair.

9. The method of claim 6 wherein said step of selecting the most significant group quality value involves selecting the maximum weighted proximity value.

10. The method of claim 3 wherein said surface of an object is a finger surface or a fingerprint surface.

11. The method of claim 2 wherein said surface of an object is a finger surface or a fingerprint surface.

12. The method of claim 1 wherein, in each of said groups, all line entries within a group are within a predetermined window on the difference plane around the lead line entry for that group.

13. The method of claim 1 wherein said first one of said sets, used in said step of verifying, is said set of reference identification points.

14. The method of claim 13 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value that is a function of the number of reference identification points.

15. The method of claim 14 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value.

16. The method of claim 13 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value.

17. The method of claim 1 wherein said weighted relationship involves determining a weighted proximity value between each pair of line entries in the group involved.

18. The method of claim 17 wherein said weighted proximity value is based on relative displacement on the difference plane of each pair.

19. The method of claim 18 wherein said weighted proximity value is further based on the relative angular displacement of each pair.

20. The method of claim 19 wherein said weighted relationship involves determining a weighted proximity value between each pair of line entries in the group involved.

21. The method of claim 20 wherein said weighted proximity value is based on relative displacement on the difference plane of each pair.

22. The method of claim 21 wherein said weighted proximity value is further based on the relative angular displacement of each pair.

23. The method of claim 22 wherein said step of selecting the most significant group quality value involves selecting the maximum weighted proximity value.

24. The method of claim 20 wherein said step of selecting the most significant group quality value involves selecting the maximum weighted proximity value.

25. The method of claim 19 wherein said step of selecting the most significant group quality value involves selecting the maximum weighted proximity value.

26. The method of claim 17 wherein said step of selecting the most significant group quality value involves selecting the maximum weighted proximity value.

27. The method of claim 1 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value that is a function of the number of identification points in said first set.

28. The method of claim 27 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value.

29. The method of claim 1 wherein said step of verifying further includes requiring that the number of line entries in the group from which the most significant group quality value is obtained exceeds a predetermined value.

30. The method of claim 1 wherein said surface of an object is a finger surface or a fingerprint surface.

31. A matcher improvement for matching a set of subject identification points against a set of reference identification points employing a difference table, comprising:

first memory means for storing a set of subject identification points,

second memory means for storing the set of reference identification points,

differencing means coupled to the outputs of said first and second storage means to provide a set of difference table entries,

means for automatically grouping the line entries on said difference table into a series of group table, each line entry on said difference table providing a lead entry for a different one of said group tables, line entries on said difference table being available for inclusion in more than one group table,

each of said group tables having no more than one line entry for each of said reference identification points,

means to provide a quality value for each group so formed by virtue of a weighted proximity relationship between each pair of entries in a group,

means to select the most significant group quality value, and

means to verify a match between the set of subject points and the set of reference points as a function of the relative magnitude of the most significant group quality value as compared to the number of identification points in a first one of said sets.

32. The improvement of claim 31 wherein that one line entry for each reference point is the one that is closest on the difference plane to the lead entry for the group.

33. The improvement of claim 32 wherein all line entries within a group are within a predetermined window on the difference plane around the lead line entry for that group.

34. The improvement of claim 33 further comprising:

second means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value based on the number of identification points in said first set.

35. The improvement of claim 34 further comprising:

third means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value.

36. The improvement of claim 33 wherein said first one of said sets, is said set of reference identification points.

37. The improvement of claim 36 further comprising:

second means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value based on the number of identification points in said first set.

38. The improvement of claim 37 further comprising:

third means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value.

39. The improvement of claim 32 wherein said most significant group quality value is the maximum weighted proximity value.

40. The improvement of claim 32 further comprising:

second means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value based on the number of identification points in said first set.

41. The improvement of claim 40 further comprising:

second means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value.

42. The improvement of claim 31 wherein all line entries within a group are within a predetermined window on the difference plane around the lead line entry for that group.

43. The improvement of claim 42 wherein said most significant group quality value is the maximum weighted proximity value.

44. The improvement of claim 31 wherein said first one of said sets, is said set of reference identification points.

45. The improvement of claim 44 wherein said most significant group quality value is the maximum weighted proximity value.

46. The improvement of claim 44 wherein said most significant group quality value is the maximum weighted proximity value.

47. The improvement of claim 31 wherein said most significant group quality value is the maximum weighted proximity value.

48. The improvement of claim 31 further comprising:

second means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value based on the number of identification points in said first set.

49. The improvement of claim 48 further comprising:

third means to further verify a match between the set of subject points and the set of reference points as a function of the number of line entries in the group from which the most significant group quality value is obtained exceeding a predetermined value.