Multi-resolutional texture analysis fingerprint liveness systems and methods
Various systems, methods, and programs embodied in computer-readable mediums are provided for fingerprint liveness detection. In one embodiment, a method for determining fingerprint liveness is provided that comprises receiving a plurality of image analysis data of a fingerprint image; condensing the plurality of image analysis data; and determining liveness of the fingerprint image based upon the condensed data.
1. A method for determining fingerprint liveness, comprising the steps of:
obtaining a plurality of image analysis data of a fingerprint image;
condensing the plurality of image analysis data; and
determining liveness of the fingerprint image based upon the condensed data, wherein determining liveness of the fingerprint image comprises the steps of:
processing the condensed data to determine a location within a cluster;
determining the distance between the condensed data location and a centroid of the cluster; and
determining liveness of the fingerprint image based upon the distance and a predefined threshold.
2. The method of claim 1 , wherein the plurality of image analysis data comprises multi-resolution texture analysis data.
3. The method of claim 1 , wherein condensing the plurality of image analysis data comprises principal component analysis.
4. The method of claim 1 , wherein the step of processing the condensed data to determine a location within a cluster is performed by fuzzy c-means clustering.
5. The method of claim 1 , further comprising the steps of:
obtaining a fingerprint image;
generating a histogram of the fingerprint image; and
determining a multi-resolution texture analysis data from the histogram.
6. The method of claim 5 , wherein multi-resolution texture analysis data comprises a first order function.
7. The method of claim 1 , further comprising the steps of:
obtaining a fingerprint image;
generating a co-occurrence matrix of the fingerprint image; and
determining a multi-resolution texture analysis data from the co-occurrence matrix.
8. The method of claim 7 , wherein multi-resolution texture analysis data comprises a second order function.
9. The method of claim 1 , further comprising the steps of:
obtaining a fingerprint image;
determining a local-ridge frequency of the fingerprint image; and
determining a local-ridge statistical data from the local-ridge frequency.
10. A system, comprising:
a processor circuit having a processor and a memory;
a detection system stored in the memory and executable by the processor, the detection system comprising:
logic that obtains a plurality of image analysis data of a fingerprint image;
logic that condenses the plurality of image analysis data; and
logic that determines liveness of the fingerprint image based upon the condensed data, wherein the logic that determines liveness of the fingerprint image comprises:
logic that processes the condensed data to determine a location within a cluster;
logic that determines the distance between the condensed data location and a centroid of the cluster; and
logic that determines liveness of the fingerprint image based upon the distance and a predefined threshold.
11. The system of claim 10 , wherein the detection system further comprises:
logic that obtains a fingerprint image;
logic that generates a histogram of the fingerprint image;
logic that generates a co-occurrence matrix of the fingerprint image; and
logic that determines a multi-resolution texture analysis data from the histogram and the co-occurrence matrix.
12. The system of claim 10 , wherein the detection system further comprises:
logic that obtains a fingerprint image;
logic that determines a local-ridge frequency of the fingerprint image; and
logic that determines a local-ridge statistical data from the local-ridge frequency.
13. The system of claim 10 , wherein logic that condenses the plurality of image analysis data comprises principal component analysis.
14. The system of claim 10 , wherein the logic that processes the condensed data to determine a location within a cluster comprises fuzzy c-means clustering.
15. The system of claim 10 , wherein the detection system further comprises:
logic that obtains a fingerprint image;
logic that generates a histogram of the fingerprint image; and
logic that determines a multi-resolution texture analysis data from the histogram.
16. The system of claim 15 , wherein multi-resolution texture analysis data comprises a first order function.
17. The system of claim 10 , wherein the detection system further comprises:
logic that obtains a fingerprint image;
logic that generates a co-occurrence matrix of the fingerprint image; and
logic that determines a multi-resolution texture analysis data from the co-occurrence matrix.
18. The system of claim 17 , wherein multi-resolution texture analysis data comprises a second order function.
19. A system, comprising:
means for obtaining a plurality of image analysis data of a fingerprint image;
means for condensing the plurality of image analysis data; and
means for determining liveness of the fingerprint image based upon the condensed data, wherein the means for determining liveness of the fingerprint image comprises:
means for processing the condensed data to determine a location within a cluster;
means for determining the distance between the condensed data location and a centroid of the cluster; and
means for determining liveness of the fingerprint image based upon the distance and a predefined threshold.
20. The system of claim 19 , wherein the system further comprises:
means for obtaining a fingerprint image;
means for generating a co-occurrence matrix of the fingerprint image; and
means for determining a multi-resolution texture analysis data from the co-occurrence matrix.
21. The system of claim 20 , wherein multi-resolution texture analysis data comprises a first order function.
22. The system of claim 19 , wherein the system further comprises:
means for obtaining a fingerprint image;
means for generating a histogram of the fingerprint image; and
means for determining a multi-resolution texture analysis data from the histogram.
23. The system of claim 22 , wherein multi-resolution texture analysis data comprises a first order function.
24. The system of claim 19 , wherein the system further comprises:
means for obtaining a fingerprint image;
means for determining a local-ridge frequency of the fingerprint image; and
means for determining a local-ridge statistical data from the local-ridge frequency.
25. The system of claim 19 , wherein the means for processing the condensed data to determine a location within a cluster includes means for fuzzy c-means clustering.