SYSTEMS AND METHODS FOR SPOOF DETECTION BASED ON GRADIENT DISTRIBUTION

• US 20180129857A1
• Filed: 12/22/2016
• Published: 05/10/2018
• Est. Priority Date: 11/10/2016
• Status: Active Grant

• Current Assignee: Synaptics Incorporated
• Original Assignee: Synaptics Incorporated
• Inventors: Bonev, Boyan
• Granted Patent: US 10,068,124 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 8/0858 involving measuring tissue layers, e.g. skin, interfaces
• CPC Class Codes: G06V 40/1365 Matching; Classification
• CPC Class Codes: G06V 40/1376 Matching features related to ridge properties or fingerprint texture
• CPC Class Codes: G06V 40/40 Spoof detection, e.g. liveness detection

Abstract

A system and method for performing spoof detection are disclosed. The method includes: receiving an input image of a biometric; generating a first filtered image by applying a first convolution to the input image based on a first convolution kernel; generating a second filtered image by applying a second convolution to the input image based on a second convolution kernel; computing a gradient residual image by subtracting, for each pixel location of the first and second filtered images, a pixel value in the second filtered image from a corresponding pixel value in the first filtered image; applying a density estimation procedure to the gradient residual image to identify areas of varied density; and, determining whether the input image is a replica of the biometric based on results of the density estimation procedure.

Claims

1. A method for spoof detection, comprising:

receiving an input image of a biometric;

generating a first filtered image by applying a first convolution to the input image based on a first convolution kernel;

generating a second filtered image by applying a second convolution to the input image based on a second convolution kernel;

computing a gradient residual image by subtracting, for each pixel location of the first and second filtered images, a pixel value in the second filtered image from a corresponding pixel value in the first filtered image;

applying a density estimation procedure to the gradient residual image to identify areas of varied density; and

determining whether the input image is a replica of the biometric based on results of the density estimation procedure.

2. The method of claim 1, wherein applying the density estimation procedure comprises:

for each pixel location of the gradient residual image, summing an amount of gradient present in a window around the pixel location to generate a density value of the window around the pixel location; and

computing an entropy value for the gradient residual image based on the density values.

3. The method of claim 2, wherein the window around each pixel location is a square-shaped window that overlaps at least one other window corresponding to a different pixel location.

4. The method of claim 2, wherein applying the density estimation procedure further comprises:

computing an average density value of the windows;

determining a count of a number of windows that have a density value that deviates from the average density values by a threshold amount; and

identifying areas of varied density based on the count.

5. The method of claim 2, wherein applying the density estimation procedure further comprises:

determining a window having a smallest density value; and

identifying areas of varied density based on the density value of the window having the smallest density value.

6. The method of claim 1, wherein the first convolution comprises a Gaussian blur, and the first convolution kernel comprises a standard deviation of a Gaussian distribution.

7. The method of claim 1, wherein the first convolution comprises a Gaussian blur, and the second convolution comprises a smoothing function other than a Gaussian blur.

8. The method of claim 1, wherein determining whether the input image is a replica of the biometric is further based on analyzing applying a density estimation procedure to one or more enrollment images of the biometric.

9. The method of claim 1, wherein the biometric comprises a fingerprint of a finger, and the replica comprises a gelatin mold, a graphite mold, or a wood glue mold of the fingerprint of the finger.

10. The method of claim 1, wherein determining whether the input image is a replica is further based on results of the density estimation procedure.

11. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, causes a computing device to perform spoof detection, by performing steps comprising:

receiving an input image of a biometric;

generating a first filtered image by applying a first convolution to the input image based on a first convolution kernel;

generating a second filtered image by applying a second convolution to the input image based on a second convolution kernel;

computing a gradient residual image by subtracting, for each pixel location of the first and second filtered images, a pixel value in the second filtered image from a corresponding pixel value in the first filtered image;

applying a density estimation procedure to the gradient residual image to identify areas of varied density; and

determining whether the input image is a replica of the biometric based on results of the density estimation procedure.

12. The computer-readable storage medium of claim 11, wherein applying the density estimation procedure comprises:

for each pixel location of the gradient residual image, summing an amount of gradient present in a window around the pixel location to generate a density value of the window around the pixel location; and

computing an entropy value for the gradient residual image based on the density values.

13. The computer-readable storage medium of claim 12, wherein the window around each pixel location is a square-shaped window that overlaps at least one other window corresponding to a different pixel location.

14. The computer-readable storage medium of claim 12, wherein applying the density estimation procedure further comprises:

computing an average density value of the windows;

determining a count of a number of windows that have a density value that deviates from the average density values by a threshold amount; and

identifying areas of varied density based on the count.

15. The computer-readable storage medium of claim 12, wherein applying the density estimation procedure further comprises:

determining a window having a smallest density value; and

identifying areas of varied density based on the density value of the window having the smallest density value.

16. The computer-readable storage medium of claim 11, wherein the first convolution comprises a Gaussian blur, and the first convolution kernel comprises a standard deviation of a Gaussian distribution.

17. The computer-readable storage medium of claim 11, wherein the first convolution comprises a Gaussian blur, and the second convolution comprises a smoothing function other than a Gaussian blur.

18. The computer-readable storage medium of claim 11, wherein determining whether the input image is a replica of the biometric is further based on analyzing applying a density estimation procedure to one or more enrollment images of the biometric.

19. The computer-readable storage medium of claim 11, wherein the biometric comprises a fingerprint of a finger, and the replica comprises a gelatin mold, a graphite mold, or a wood glue mold of the fingerprint of the finger.

20. A device, comprising:

a biometric sensor; and

a processing system configured to;

receive, from the biometric sensor, an input image of a biometric;

generate a first filtered image by applying a first convolution to the input image based on a first convolution kernel;

generate a second filtered image by applying a second convolution to the input image based on a second convolution kernel;

compute a gradient residual image by subtracting, for each pixel location of the first and second filtered images, a pixel value in the second filtered image from a corresponding pixel value in the first filtered image;

apply a density estimation procedure to the gradient residual image to identify areas of varied density; and

determine whether the input image is a replica of the biometric based on results of the density estimation procedure.