TRANSFORMED REPRESENTATION FOR FINGERPRINT DATA WITH HIGH RECOGNITION ACCURACY

• US 20170344805A1
• Filed: 08/11/2017
• Published: 11/30/2017
• Est. Priority Date: 06/08/2015
• Status: Active Grant

• Current Assignee: HID Global Corporation (Assa Abloy AB)
• Original Assignee: Cross Match Technologies Incorporated (Assa Abloy AB)
• Inventors: YANG, BIAN, LI, GUOQIANG
• Granted Patent: US 10,169,638 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06V 40/1347 Preprocessing; Feature extraction
• CPC Class Codes: G06V 40/1353 Extracting features related to minutiae or pores
• CPC Class Codes: G06V 40/1371 Matching features related to minutiae or pores
• CPC Class Codes: H04L 9/3231 Biological data, e.g. fingerprint, voice or retina network architectures or network communication protocols for supporting authentication of entities using biometrical features in a packet data network H04L63/0861

Abstract

The invention provides a computing device for processing fingerprint template data, wherein the computing device comprises modules configured for generating transformed fingerprint template data from original fingerprint minutiae template data, in which minutiae are represented at least by Cartesian or polar coordinates and an inherent minutiae direction, the computing device being configured to generate for each minutia of the original fingerprint minutiae data corresponding mega-vicinity data. The computing device may further comprise a comparator module being configured to compare two transformed fingerprint template data that comprises, for each minutia, mega-vicinity data of a first fingerprint and a second fingerprint. The computing device may further comprise a biometrics-secret binding module for protecting.

Claims

1. A system for processing fingerprint template data, comprising:

a first module configured to define for the minutia m in the original fingerprint minutiae template data the corresponding mega-vicinity as a vicinity area surrounding the minutia m as the center;

a second module configured to align geometrically all other minutiae inside the defined mega-vicinity with respect to the minutia m, wherein the minutia m defines the origin of a Cartesian and polar, respectively, coordinate system x-m-y, wherein the direction of the minutia m of the original fingerprint minutiae data defines the x-axis and the direction of the zero angle, respectively, of the Cartesian and polar, respectively, coordinate system;

a third module configured to check for each other minutia in the original fingerprint minutiae data, if its location is within the vicinity area; and

a fourth module configured to form the mega-vicinity data for the minutia m as comprising first layer data comprising all minutiae located inside the vicinity area and second layer data comprising the respective N closest neighboring minutiae around each of the minutiae in the first layer data, wherein N is a predetermined number.

2. The system of claim 1, wherein the system further comprises a Fifth module configured to reduce the data size of the generated transformed fingerprint template data by quantizing the minutia data to limited discrete bins.

3. The system of claim 1, further comprising a fingerprint comparator module being configured to compare the transformed fingerprint template data that comprises, for each minutia, mega-vicinity data, obtainable by the system of claim 1, of a first fingerprint and a second fingerprint, the comparator module being configured to

(i) determine minutiae match cases in the first layer L1 of two aligned mega-vicinities from the first transformed fingerprint template data and the second transformed fingerprint template data, and

(ii) compare, for each match case in the first layer L1, a predetermined number N of closest neighboring minutiae of the second layer L2 around each of the two matched minutiae, wherein N>

0, and

(iii) determine for each match case a comparison score.

4. The system of claim 3, wherein the fingerprint comparator module is further configured to combine the comparison scores of the comparison in the first layer L1 and the comparison scores of the comparison in the second layer L2 into a fused score for the two mega-vicinities.

5. The system of claim 4, wherein the fingerprint comparator module is further configured to weigh the fused comparison score by the sum of the distances from a matched minutia to its L neighbors, wherein L>

0.

6. The system of claim 5, wherein the fingerprint comparator module is further configured to generate a mega-vicinity comparison score for a mega-vicinity and to weigh the comparison score for a particular mega-vicinity by the ratio of the matched minutiae in the first layer L1 of the particular mega-vicinity and the total number of minutiae in the first layer.

7. The system of claim 6, wherein the fingerprint comparator module is further configured to generate a normalized final comparison score between the two transformed fingerprint templates by checking not only the highest comparison score between a probe mega-vicinity and a group of reference mega-vicinities, but also to check other non-highest comparison scores.

8. The system of claim 7, wherein the system further comprises a biometrics-secret binding module configured to encrypt plain mega-vicinity data into protected mega-vicinity data.

9. The system of claim 8, wherein the system is part of a control unit of one of the following devices from the group comprising:

a secure login device to computer systems, user identification device at kiosks or automatic teller machines, biometric door locks, credit card terminals, identity authentication protocols and devices, weapon activation control devices, theft protection in any electronic apparatus.

10. The system of one of claim 9, wherein the system is arranged by corresponding hardware for at least one of:

capturing fingerprint images, processing fingerprint images, extracting features from fingerprint images, generating transformed fingerprint template data from original fingerprint minutiae template data, storing fingerprint template data for comparison, and comparing or indexing fingerprint template data.

11. A method for processing fingerprint template data, including the steps of:

providing a first module configured to define for the minutia m in the original fingerprint minutiae template data the corresponding mega-vicinity as a vicinity area surrounding the minutia m as the center;

providing a second module configured to align geometrically all other minutiae inside the defined mega-vicinity with respect to the minutia m, wherein the minutia m defines the origin of a Cartesian and polar, respectively, coordinate system x-m-y, wherein the direction of the minutia m of the original fingerprint minutiae data defines the x-axis and the direction of the zero angle, respectively, of the Cartesian and polar, respectively, coordinate system;

providing a third module configured to check for each other minutia in the original fingerprint minutiae data, if its location is within the vicinity area; and

providing a fourth module configured to form the mega-vicinity data for the minutia m as comprising first layer data comprising all minutiae located inside the vicinity area and second layer data comprising the respective N closest neighboring minutiae around each of the minutiae in the first layer data, wherein N is a predetermined number.

12. The method of claim 11, wherein the method further comprises the steps of providing a fifth module configured to reduce the data size of the generated transformed fingerprint template data by quantizing the minutia data to limited discrete bins.

13. The method of claim 11, further comprising the steps of providing a fingerprint comparator module being configured to compare the transformed fingerprint template data that comprises, for each minutia, mega-vicinity data, obtainable by the method of claim 21, of a first fingerprint and a second fingerprint, the comparator module being configured to

(i) determine minutiae match cases in the first layer L1 of two aligned mega-vicinities from the first transformed fingerprint template data and the second transformed fingerprint template data, and

(ii) compare, for each match case in the first layer L1, a predetermined number N of closest neighboring minutiae of the second layer L2 around each of the two matched minutiae, wherein N>

0, and

(iii) determine for each match case a comparison score.

14. The method of claim 13, wherein the fingerprint comparator module is further configured to combine the comparison scores of the comparison in the first layer L1 and the comparison scores of the comparison in the second layer L2 into a fused score for the two mega-vicinities.

15. The method of claim 14, wherein the fingerprint comparator module is further configured to weigh the fused comparison score by the sum of the distances from a matched minutia to its L neighbors, wherein L>

0.

16. The method of claim 15, wherein the fingerprint comparator module is further configured to generate a mega-vicinity comparison score for a mega-vicinity and to weigh the comparison score for a particular mega-vicinity by the ratio of the matched minutiae in the first layer L1 of the particular mega-vicinity and the total number of minutiae in the first layer.

17. The method of claim 16, wherein the fingerprint comparator module is further configured to generate a normalized final comparison score between the two transformed fingerprint templates by checking not only the highest comparison score between a probe mega-vicinity and a group of reference mega-vicinities, but also to check other non-highest comparison scores.

18. The method of claim 17, wherein the method further comprises a biometrics-secret binding module configured to encrypt plain mega-vicinity data into protected mega-vicinity data.

19. The method of claim 18, wherein the method is part of a control unit of one of the following devices from the group comprising:

a secure login device to computer systems, user identification device at kiosks or automatic teller machines, biometric door locks, credit card terminals, identity authentication protocols and devices, weapon activation control devices, theft protection in any electronic apparatus.

20. The method of claim 19, wherein the method is arranged by providing corresponding hardware for at least one of:

capturing fingerprint images, processing fingerprint images, extracting features from fingerprint images, generating transformed fingerprint template data from original fingerprint minutiae template data, storing fingerprint template data for comparison, and comparing or indexing fingerprint template data.