Systems and methods for spoof detection based on gradient distribution
First Claim
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,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;
computing an average density value of the density values of the windows;
determining a count of a number of windows that have a density value that deviates from the average density value by a threshold amount; and
identifying areas of varied density based on the count.
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Accused Products
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.
6 Citations
15 Claims
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1. A method for spoof detection, comprising:
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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, 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; computing an average density value of the density values of the windows; determining a count of a number of windows that have a density value that deviates from the average density value by a threshold amount; and identifying areas of varied density based on the count. - View Dependent Claims (2, 3, 4, 5)
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6. 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:
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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, 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; 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. - View Dependent Claims (7, 8, 9, 10)
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11. A device, comprising:
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a biometric sensor; and a memory storing instructions; and a processor configured to execute the instructions to cause the device 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, 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; computing an average density value of the density values of the windows; determining a count of a number of windows that have a density value that deviates from the average density value by a threshold amount; and identifying areas of varied density based on the count. - View Dependent Claims (12, 13, 14, 15)
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Specification