Multi-mode digital image processing method for detecting eyes
First Claim
1. A digital image processing method for detecting human eyes in a digital image, comprising the steps of:
- detecting iris pixels;
clustering the iris pixels;
determining the number of iris pixel clusters;
selecting at least one of the following methods to identify eye positions in an image;
i) applying geometric reasoning to detect eye positions using the iris pixel clusters;
ii) applying a summation of squared difference to detect eye positions based the iris color pixel clusters;
iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;
wherein the applying step is selected on the basis of the number of iris pixel clusters.
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Abstract
The present invention comprises a digital image processing method for detecting human eyes in a digital image. This method comprises the steps of: detecting iris pixels in the image; clustering the iris pixels, and selecting at least one of the following methods to identify eye positions: applying geometric reasoning to detect eye positions using the iris pixel clusters; applying a summation of squared difference method using the iris pixel clusters to detect eye positions; and applying a summation of squared difference method to detect eye positions from the pixels in the image. The method applied is selected on the basis of the number of iris pixel clusters. In another embodiment, the present invention also comprises a computer program product.
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Citations
42 Claims
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1. A digital image processing method for detecting human eyes in a digital image, comprising the steps of:
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detecting iris pixels;
clustering the iris pixels;
determining the number of iris pixel clusters;
selecting at least one of the following methods to identify eye positions in an image;
i) applying geometric reasoning to detect eye positions using the iris pixel clusters;
ii) applying a summation of squared difference to detect eye positions based the iris color pixel clusters;
iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;
wherein the applying step is selected on the basis of the number of iris pixel clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
dividing the image pixels into left-half pixels and right-half pixels;
locating the most likely left eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the left-half pixels; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the right-half pixels.
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4. The method of claim 3 further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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5. The method of claim 3 further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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6. The method of claim 1, wherein applying step i) is selected when at least two iris pixel clusters are detected.
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7. The method of claim 6, wherein method i) is first applied and method ii) is subsequently applied in the event that method i) does not detect at least two eye positions.
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8. The method of claim 7, wherein method ii) does not detect eye positions and wherein method iii) is then applied.
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9. The method claimed in claim 8, wherein the step of applying a summation of squared difference method to detect eye positions from the pixels in the image comprises the steps of:
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dividing the image pixels into left-half pixels and right-half pixels;
locating the most likely left eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the left-half pixels; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the right-half pixels.
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10. The computer program product of claim 9, wherein the step of applying geometric reasoning using the detected iris color pixels comprises the steps of:
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finding the center of each iris pixel cluster;
dividing the iris pixel clusters into left-half iris pixel clusters and right-half iris pixel clusters; and
detecting a pair of eyes based on the geometric relationship between the left-half iris pixel clusters and the right-half iris pixel clusters.
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11. The method of claim 7, wherein the step of applying the summation squared difference method to detect eye positions based upon the iris pixel clusters, comprises the steps of:
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finding the center of each iris pixel cluster;
defining a window of pixels surrounding each of the centers of the iris pixel clusters in the image;
dividing the iris pixel clusters into left-half pixel clusters and right-half iris pixel clusters;
locating the most likely left eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a left-half iris pixel cluster; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a right-half iris pixel cluster.
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12. The method of claim 11 further comprising the steps of detecting a skin color region in the image, and dividing the skin color region into a left-half region and right-half region wherein the iris pixel clusters are divided into left-half iris pixel clusters and right-half iris pixel clusters based upon the region in which they are located.
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13. The method of claims 6, wherein the step of applying geometric reasoning using the detected iris color pixels comprises the steps of:
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finding the center of each iris pixel cluster;
dividing the iris pixel clusters into left-half pixel clusters and right-half pixel clusters; and
detecting a pair of eyes based on the geometric relationship between the iris pixel clusters.
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14. The method of claim 1, wherein the step of applying geometric reasoning using the detected iris color pixels comprises the steps of:
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finding the center of each iris pixel cluster;
dividing the iris pixel clusters into left-half pixel clusters and right-half pixel clusters; and
detecting a pair of eyes based on the geometric relationship between the iris pixel clusters.
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15. The method of claim 1, wherein the step of applying the summation squared difference method to detect eye positions based upon the iris pixel clusters, comprises the steps of:
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finding the center of each iris pixel cluster;
defining a window of pixels surrounding each of the centers of the iris pixel clusters in the image;
dividing the iris pixel clusters into left-half pixel clusters and right-half iris pixel clusters;
locating the most likely left eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a left-half iris pixel cluster; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a right-half iris pixel cluster.
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16. The method of claim 15 further comprising the steps of detecting a skin color region in the image, and dividing the skin color region into a left-half region and right-half region wherein the iris pixel clusters are divided into left-half iris pixel clusters and right-half iris pixel clusters based upon the region in which they are located.
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17. The method of claim 1, wherein the step of applying a summation of squared difference method to detect eye positions from the pixels in the image comprises the steps of:
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dividing the image pixels into left-half pixels and right-half pixels;
locating the most likely left eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the left-half pixels; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the right-half pixels.
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18. The method of claim 17, further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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19. The method of claim 1 further comprising the step of validating iris pixel clusters, wherein the selection of the method to be applied is made based upon the number of valid clusters.
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20. A computer program product for detecting human eyes in a digital image, the computer program product comprising a computer readable storage medium having a computer program stored thereon for performing the step of:
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detecting iris pixels;
clustering the iris pixels;
selecting at least one of the following methods to identify eye positions in the image;
i) applying geometric reasoning to detect eye positions using the iris pixel clusters;
ii) applying a summation of squared difference method to detect eye positions based upon the iris pixel clusters; and
iii) applying a summation of squared difference method using non-iris pixels to detect eye positions;
wherein the method applied is selected on the basis of the number of valid iris pixel clusters. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
finding the center of each cluster;
defining a window of pixels surrounding each of the centers of the pixel clusters in the image;
dividing the iris pixel clusters into left-half pixel clusters and right-half pixel clusters;
locating the most likely left eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the pixels in each of the windows surrounding a left-half iris pixel cluster; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a right-half iris pixel cluster.
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26. The computer program product of claim 25 further comprising the steps of detecting a skin color region in the image, and dividing the skin color region into a left-half region and a right-half region wherein the iris pixel clusters are divided into left-half iris pixel clusters and right-half iris pixel clusters based upon the region in which they are located.
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27. The computer program product of claim 20, wherein the step of applying geometric reasoning using the detected iris color pixels comprises the steps of:
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finding the center of each iris pixel cluster;
dividing the iris pixel clusters into left-half iris pixel clusters and right-half iris pixel clusters; and
detecting a pair of eyes based on the geometric relationship between the left-half iris pixel clusters and the right-half iris pixel clusters.
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28. The computer program product of claim 27 wherein the step of detecting iris color pixels using a Bayes model comprises measuring the red intensity of the pixels in the skin color region;
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determining the probability that each pixel is an iris based upon the red intensity of the pixel;
determining the probability that each pixel is not an iris based upon the red intensity of the pixel; and
applying the Bayes model to the probability that the pixel is an iris, the probability that the pixel is not an iris, the probability of the occurrence of an iris in the skin colored region and probability of the occurrence of a non-iris pixel in the skin colored region.
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29. The computer program product of claim 28, further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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30. The computer program product of claim 27 wherein the step of detecting iris color pixels using a Bayes model comprises measuring the red intensity of the pixels in the skin color region;
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determining the probability that each pixel is an iris based upon the red intensity of the pixel;
determining the probability that each pixel is not an iris based upon the red intensity of the pixel; and
applying the Bayes model to the probability that the pixel is an iris, the probability that the pixel is not an iris, the probability of the occurrence of an iris in the skin colored region and probability of the occurrence of a non-iris pixel in the skin colored region.
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31. The computer program product of claim 30, further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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32. The computer program product of claim 20, wherein the step of applying the summation squared method to detect eye positions based upon the iris pixel clusters, comprises the steps of:
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finding the center of each cluster;
defining a window of pixels surrounding each of centers of the pixel clusters in the image;
dividing the iris pixel clusters into left-half pixel clusters and right-half pixel clusters;
locating the most likely left eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the pixels in each of the windows surrounding a left-half iris pixel cluster; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patches of the image centered at each of the pixels in each of the windows surrounding a right-half iris pixel cluster.
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33. The computer program product of claim 32 further comprising the steps of detecting a skin color region in the image, and dividing the skin color region into a left-half region and a right-half region wherein the iris pixel clusters are divided into left-half iris pixel clusters and right-half iris pixel clusters based upon the region in which they are located.
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34. The computer program product claimed in claim 20 wherein the step of applying a summation of squared difference method using image pixels to detect eye positions comprises the steps of:
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dividing the pixels in the image into left-half pixels and right-half pixels;
locating the most likely left-eye position based on the summation of squared difference between an average eye and patch of the image centered at each of left-half pixels; and
locating the most likely right eye position based on the summation of squared difference between an average eye and patch of the image centered at each of the right-half pixels.
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35. The computer program product of claim 34, further comprising detecting a skin color region in the image, wherein the summation of the squared difference method is only applied to pixels within the skin color region.
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36. A digital image processing method for detecting human eyes comprising the steps of:
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detecting iris pixels;
clustering iris pixels;
determining the number of iris pixel clusters;
selecting a method for detecting eye positions based upon the number of iris pixel clusters; and
using the selected method to detect eye positions wherein the step of selecting a method for detecting eye positions based upon the number of iris pixel clusters comprises selecting a series of eye detection steps. - View Dependent Claims (37, 38, 39, 40, 41, 42)
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Specification