Regional fingerprint liveness detection 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 fingerprint image; extracting a region of interest from the fingerprint image; generating a histogram of the region of interest; determining a statistical feature of the histogram; and determining liveness of the fingerprint image based upon the statistical feature.
1. A method for determining fingerprint liveness, comprising the steps of:
obtaining a fingerprint image;
extracting a region of interest from the fingerprint image, comprising:
dividing the fingerprint image into blocks;
determining a variance for each image block;
removing image blocks based upon the variance and a predetermined threshold;
determining a center point of the remaining image; and
extracting the region of interest about the center point;
generating a histogram of the region of interest;
determining a statistical feature of the histogram; and
determining liveness of the fingerprint image based upon the statistical feature.
2. The method of claim 1 , wherein the histogram is based upon grayscale variation of pixels within the region of interest.
3. The method of claim 1 , wherein the step of determining a statistical feature of the histogram further comprises:
summing histogram distribution values over a predefined range of the histogram; and
normalizing the sum of histogram distribution values.
4. The method of claim 1 , wherein the step of determining liveness of the fingerprint image further comprises performing a neural network analysis of the statistical feature.
5. The method of claim 1 , wherein the step of determining liveness of the fingerprint image further comprises performing a classification tree analysis of the statistical feature.
6. The method of claim 5 , wherein the classification tree analysis is based upon J48 classification.
7. The method of claim 1 , wherein the variance is based upon intensity variation of pixels within each image block.
8. The method of claim 1 , wherein the region of interest is a predefined area centered at the center point.
9. The method of claim 8 , wherein the predefined area is square.
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 receives a fingerprint image;
logic that extracts a region of interest from the fingerprint image, comprising:
logic that divides the fingerprint image into blocks;
logic that determines a variance for each image block;
logic that removes image blocks based upon the variance and a predetermined threshold;
logic that determines a center point of the remaining image; and
logic that extracts a region of interest about the center point;
logic that generates a histogram of the region of interest;
logic that determines a statistical feature of the histogram; and
logic that determines liveness of the fingerprint image based upon the statistical feature.
11. The system of claim 10 , wherein the variance is based upon intensity variation of pixels within each image block.
12. The system of claim 10 , wherein the logic that determines a statistical feature of the histogram further comprises:
logic that sums histogram distribution values over a predefined range of the histogram; and
logic that normalizes the sum of histogram distribution values.
13. The system of claim 10 , wherein the logic that determines liveness of the fingerprint image further comprises logic to perform a classification tree analysis of the statistical feature.
14. The system of claim 10 , wherein the histogram is based upon grayscale variation of pixels within the region of interest.
15. The system of claim 10 , wherein the region of interest is a predefined area centered at the center point.
16. The system of claim 10 , wherein the logic that determines liveness of the fingerprint image further comprises logic to perform a neural network analysis of the statistical feature.
17. A system, comprising:
means for obtaining a fingerprint image;
means for extracting a region of interest from the fingerprint image, comprising:
means for dividing the fingerprint image into blocks;
means for determining a variance for each image block;
means for removing image blocks based upon the variance and a predetermined threshold;
means for determining a center point of the remaining image; and
means for extracting a region of interest about the center point;
means for generating a histogram of the region of interest;
means for determining a statistical feature of the histogram; and
means for determining liveness of the fingerprint image based upon the statistical feature.
18. The system of claim 17 , wherein the means for determining a statistical feature of the histogram further comprises:
means for summing histogram distribution values over a predefined range of the histogram; and
means for normalizing the sum of histogram distribution values.
19. The system of claim 17 , wherein the means for determining liveness of the fingerprint image further comprises means for performing a classification tree analysis of the statistical feature.
20. The system of claim 17 , wherein the histogram is based upon grayscale variation of pixels within the region of interest.