LEVEL 3 FEATURES FOR FINGERPRINT MATCHING

• US 20070230754A1
• Filed: 03/28/2007
• Published: 10/04/2007
• Est. Priority Date: 03/30/2006
• Status: Abandoned Application

• Current Assignee: Board of Trustees Operating
• Original Assignee: Board of Trustees Operating
• Inventors: Chen, Yi, Jain, Anil K., Demirkus, Meltem
• Application Number:
  US11/692,524
• Publication Number:
  US 20070230754A1
• Time in Patent Office: Days
• US Class Current: 382/125
• CPC Class Codes: G06V 40/1371 Matching features related to minutiae or pores

Abstract

Fingerprint recognition and matching systems and methods are described that utilize features at all three fingerprint friction ridge detail levels, i.e., Level 1, Level 2 and Level 3, extracted from 1000 ppi fingerprint scans. Level 3 features, including but not limited to pore and ridge contour characteristics, were automatically extracted using various filters (e.g., Gabor filters, edge detector filters, and/or the like) and transforms (e.g., wavelet transforms) and were locally matched using various algorithms (e.g., the iterative closest point (ICP) algorithm). Because Level 3 features carry significant discriminatory and complementary information, there was a relative reduction of 20% in the equal error rate (EER) of the matching system when Level 3 features were employed in combination with Level 1 and Level 2 features, which were also automatically extracted. This significant performance gain was consistently observed across various quality fingerprint images.

Claims

1. A method for extracting information from a fingerprint image, wherein the fingerprint image contains Level 1, Level 2 and Level 3 features, comprising:

applying a first filter to the fingerprint image to extract the location of any ridges;

wherein a first enhanced fingerprint image is produced by the application of the first filter; and

applying a second filter to the fingerprint image to extract the location of any pores;

wherein a response is produced by the application of the second filter.

2. The invention according to claim 1, wherein the response is combined with the first enhanced fingerprint image to produce a second enhanced fingerprint image, wherein the location of the ridges and pores are enhanced.

3. The invention according to claim 1, wherein the first filter is a Gabor filter.

4. The invention according to claim 1, wherein the second filter is a band pass filter.

5. The invention according to claim 4, wherein the band pass filter is a wavelet transform.

6. The invention according to claim 5, wherein the wavelet transform is a Mexican Hat wavelet transform.

7. The invention according to claim 1, wherein the response is subtracted from the first enhanced image to produce a third enhanced fingerprint image, wherein any contours of the ridges are enhanced.

8. The invention according to claim 7, wherein the third enhanced fingerprint image is binarized to produce a fourth enhanced fingerprint image.

9. The invention according to claim 8, wherein the fourth enhanced fingerprint image is convolved to produce a fifth enhanced fingerprint image.

10. The invention according to claim 1, wherein the fingerprint image is a 1000 pixel per square inch image.

11. A method for determining a match between a first fingerprint image and a second fingerprint image, wherein the first and second fingerprint images contain Level 1, Level 2 and Level 3 features, comprising:

comparing the Level 1 features of the first and second fingerprint images;

if no match exists between the Level 1 features of the first and second fingerprint images, then comparing the Level 2 features of the first and second fingerprint images; and

if no match exists between the Level 2 features of the first and second fingerprint images, then comparing the Level 3 features of the first and second fingerprint images;

wherein the step of comparing the Level 3 features of the first and second fingerprint images comprises;

applying a first filter to both of the first and second fingerprint images to extract the location of any ridges;

wherein third and fourth enhanced fingerprint images are produced by the application of the first filter to the first and second fingerprint images respectively; and

applying a second filter to both of the first and second fingerprint images to extract the location of any pores;

wherein first and second responses are produced by the application of the second filter to the first and second fingerprint images respectively.

12. The invention according to claim 11, wherein the first response is combined with the first enhanced fingerprint image to produce a third enhanced fingerprint image, wherein the location of the ridges and pores are enhanced or wherein the second response is combined with the second enhanced fingerprint image to produce a fourth enhanced fingerprint image.

13. The invention according to claim 11, wherein the first filter is a Gabor filter.

14. The invention according to claim 11, wherein the second filter is a band pass filter.

15. The invention according to claim 14, wherein the band pass filter is a wavelet transform.

16. The invention according to claim 15, wherein the wavelet transform is a Mexican Hat wavelet transform.

17. The invention according to claim 11, wherein the first response is subtracted from the first enhanced image to produce a fifth enhanced fingerprint image, wherein any contours of the ridges are enhanced or wherein the second response is subtracted from the second enhanced image to produce a sixth enhanced fingerprint image, wherein any contours of the ridges are enhanced.

18. The invention according to claim 17, wherein either of the fifth or sixth enhanced fingerprint images are binarized to produce a seventh enhanced fingerprint image.

19. The invention according to claim 18, wherein the seventh enhanced fingerprint image is convolved to produce an eighth enhanced fingerprint image.

20. The invention according to claim 11, wherein either of the first or second fingerprint images is a 1000 pixel per square inch image.

21. The invention according to claim 11, wherein the Level 3 features of the first and second fingerprint images are compared with an iterative closest point algorithm.

22. The invention according to claim 22, wherein the iterative closest point algorithm was applied to a local region of either the first or second fingerprint images.

23. A method for determining a match between a first fingerprint image and a second fingerprint image, wherein the first and second fingerprint images contain Level 1, Level 2 and Level 3 features, comprising:

comparing the Level 1 features of the first and second fingerprint images;

if no match exists between the Level 1 features of the first and second fingerprint images, then comparing the Level 2 features of the first and second fingerprint images; and

if no match exists between the Level 2 features of the first and second fingerprint images, then comparing the Level 3 features of the first and second fingerprint images;

wherein the step of comparing the Level 3 features of the first and second fingerprint images comprises;

applying a Gabor filter to both of the first and second fingerprint images to extract the location of any ridges;

wherein third and fourth enhanced fingerprint images are produced by the application of the first filter to the first and second fingerprint images respectively; and

applying a band pass filter to both of the first and second fingerprint images to extract the location of any pores;

wherein first and second responses are produced by the application of the second filter to the first and second fingerprint images respectively.

wherein the Level 3 features of the first and second fingerprint images are compared with an iterative closest point algorithm.

24. The invention according to claim 23, wherein the first response is combined with the first enhanced fingerprint image to produce a third enhanced fingerprint image, wherein the location of the ridges and pores are enhanced or wherein the second response is combined with the second enhanced fingerprint image to produce a fourth enhanced fingerprint image.

25. The invention according to claim 23, wherein the band pass filter is a wavelet transform.

26. The invention according to claim 25, wherein the wavelet transform is a Mexican Hat wavelet transform.

27. The invention according to claim 23, wherein the first response is subtracted from the first enhanced image to produce a fifth enhanced fingerprint image, wherein any contours of the ridges are enhanced or wherein the second response is subtracted from the second enhanced image to produce a sixth enhanced fingerprint image, wherein any contours of the ridges are enhanced.

28. The invention according to claim 27, wherein either of the fifth or sixth enhanced fingerprint images is binarized to produce a seventh enhanced fingerprint image.

29. The invention according to claim 28, wherein the seventh enhanced fingerprint image is convolved to produce an eighth enhanced fingerprint image.

30. The invention according to claim 23, wherein either of the first or second fingerprint images is a 1000 pixel per square inch image.

31. The invention according to claim 23, wherein the iterative closest point algorithm was applied to a local region of either the first or second fingerprint images.