Methods for finger biometric processing and associated finger biometric sensors

• US 7,616,787 B2
• Filed: 10/01/2004
• Issued: 11/10/2009
• Est. Priority Date: 10/01/2003
• Status: Expired due to Fees

• Current Assignee: Apple Inc.
• Original Assignee: AuthenTec Incorporated (Apple Inc.)
• Inventors: Boshra, Michael
• Primary Examiner(s): Mehta; Bhavesh M
• Assistant Examiner(s): LIEW, ALEX KOK SOON
• Application Number:
  US10/956,491
• Publication Number:
  US 20050129291A1
• Time in Patent Office: 1,866 Days
• Field of Search: None
• US Class Current: 382/124
• CPC Class Codes: G06V 40/1335 Combining adjacent partial images (e.g. slices) to create a composite input or reference pattern; Tracking a sweeping finger movement

Abstract

A method is for processing a plurality of finger biometric enrollment data sets that accounts for uncertainty in alignment between finger biometric enrollment data sets. The method may include generating a respective estimated physical transformation between each pair of finger biometric enrollment data sets, and generating a respective uncertainty for each estimated physical transformation. The method may also include associating the respective estimated physical transformation and the uncertainty for each estimated physical transformation with each pair of finger biometric enrollment data sets to define a logical finger biometric enrollment graph. The logical finger biometric enrollment graph is readily used for subsequent accurate matching in either touch sensor applications or slide sensor applications.

Claims

1. A method for finger biometric processing comprising:

generating more than one pair of finger biometric enrollment data sets based upon successive placements of a user'"'"'s finger upon a finger biometric touch sensor;

generating a respective estimated physical transformation between each pair of finger biometric enrollment data sets;

generating a respective uncertainty for each estimated physical transformation; and

associating the respective estimated physical transformation and the respective uncertainty with each pair of finger biometric enrollment data sets to define a logical finger biometric enrollment graph.

2. A method according to claim 1 further comprising generating a respective confidence score for an alignment between each pair of finger biometric enrollment data sets;

and wherein associating further comprises associating the respective confidence score with each pair of finger biometric enrollment data sets to further define the logical finger biometric enrollment graph.

3. A method according to claim 1 further comprising:

generating more than one finger biometric verify data set; and

comparing each of the finger biometric verify data set with the logical finger biometric enrollment graph to determine a match therewith.

4. A method according to claim 3 wherein comparing comprises:

determining which finger biometric enrollment data set produces a highest match value with each finger biometric verify data set thereby defining an anchor node of the logical finger biometric enrollment graph; and

determining a match score between each finger biometric verify data set and at least one other finger biometric enrollment data set.

5. A method according to claim 4 further comprising removing redundant data of the anchor node from the more than one other finger biometric enrollment data set prior to determining the match score.

6. A method according to claim 1 wherein the more than one pair of finger biometric enrollment data sets are generated based upon sliding placement of a user'"'"'s finger upon a finger biometric slide sensor that generates a series of finger biometric enrollment data sets during sliding.

7. A method according to claim 6 further comprising:

generating a series of finger biometric verify data sets using the finger biometric slide sensor;

generating a respective estimated physical transformation between each pair of finger biometric verify data sets;

generating a respective uncertainty for each estimated physical transformation; and

associating the respective estimated physical transformation and the respective uncertainty with each of the finger biometric verify data sets to define a logical finger biometric verify graph.

8. A method according to claim 7 wherein generating the respective uncertainty for each estimated physical transformation between each pair of the finger biometric verify data sets comprises using more than one uniform and Gaussian probability distribution function.

9. A method according to claim 7 further comprising generating an initial alignment between the logical finger biometric enrollment graph and the logical finger biometric verify graph.

10. A method according to claim 9 further comprising comparing the logical finger biometric verify graph with the logical finger biometric enrollment graph based upon the initial alignment to determine a match therewith.

11. A method according to claim 10, further comprising removing redundant data from more than one of the logical finger biometric enrollment graph and logical finger biometric verify graph.

12. A method according to claim 10, wherein comparing the logical finger biometric verify graph with the logical finger biometric enrollment graph comprises:

generating a respective uncertainty in alignment between more than one node in the logical finger biometric verify graph and at least one node in the logical finger biometric enrollment graph;

performing constrained node-to-graph matching based upon the respective uncertainty in alignment; and

generating an overall graph matching score based upon the constrained node-to-graph matching.

13. A method according to claim 1 wherein generating the respective estimated physical transformation comprises correlating respective sub-regions in each pair of finger biometric enrollment data sets.

14. A method according to claim 1 wherein each finger biometric enrollment data set comprises a finger biometric enrollment image data set, and further comprising subjecting each finger biometric enrollment image data set to at least one of image enhancement for reducing noise content, and image warping to account or non-instantaneous sensor operation or non-linear deformations.

15. A method according to claim 1 wherein the respective estimated physical transformation comprises at least one of a horizontal translation, a vertical translation, and a rotation.

16. A method according to claim 1 wherein each finger biometric enrollment data set comprises more than one of a finger image data set and a finger biometric feature set.

17. A method according to claim 1 wherein each finger biometric enrollment data set comprises a combination of finger biometric enrollment data subsets.

18. A method according to claim 1, wherein generating the respective estimated physical transformations is based upon alignment of respective edges of each pair of finger biometric enrollment data sets.

19. A method according to claim 1 wherein each pair of finger biometric enrollment data sets is generated by an integrated circuit finger biometric sensor.

20. A method for processing a plurality of finger biometric data sets comprising:

generating more than one pair of finger biometric data sets based upon successive placements of a user'"'"'s finger upon an integrated circuit finger biometric sensor;

generating a respective estimated physical transformation between each pair of finger biometric data sets;

generating a respective uncertainty for each estimated physical transformation; and

associating the respective estimated physical transformation and the respective uncertainty with each pair of finger biometric data sets to define a logical finger biometric graph.

21. A method according to claim 20, further comprising generating a respective confidence score for an alignment between each pair of finger biometric data sets;

and wherein associating further comprises associating the respective confidence score with each pair of finger biometric data sets to define the logical finger biometric graph.

22. A method according to claim 20 wherein the respective estimated physical transformation comprises at least one of a horizontal translation, a vertical translation, and a rotation.

23. A method according to claim 20, wherein each finger biometric data set comprises more than one of a finger image data set and a finger biometric feature set.

24. A method according to claim 20, wherein generating the respective estimated physical transformation is based upon alignment of respective edges of each pair of finger biometric data sets.