Digital image processing method and computer program product for detecting human irises in an image
First Claim
1. A digital image processing method for detecting human irises in a digital image, comprising the steps of:
- measuring the red intensity of the pixels in the image;
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
determining whether each pixel is an iris by analyzing the relationship between the probability that the pixel is an iris and probability that the pixel is not an iris.
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Abstract
A digital image processing method is taught for detecting human irises in a digital image. The method comprises the steps measuring the red intensity of the pixels in the image, 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 determining whether the pixel is an iris by analyzing the relationship between the probability that the pixel is an iris and the probability that the pixel is not an iris. In one embodiment of the present invention, the determination as to whether a pixel is an iris pixel is then made based upon the application of a Bayes model to the probability that the pixel is not an iris, the probability of the occurrence of an iris in the identified region and probability of the occurrence of a non-iris pixel in the identified region. In another embodiment of the present invention, the method comprises the steps of finding an oval shaped skin color region, detecting iris color pixels in the oval shaped skin color region, detecting iris color pixels in the oval shaped region using a Bayes model and locating eye positions based upon the detected iris color pixels.
A computer program product for performing these methods is also taught.
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Citations
26 Claims
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1. A digital image processing method for detecting human irises in a digital image, comprising the steps of:
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measuring the red intensity of the pixels in the image;
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
determining whether each pixel is an iris by analyzing the relationship between the probability that the pixel is an iris and probability that the pixel is not an iris. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for detecting human irises and eyes in a digital image comprising the steps of:
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finding a skin color region;
detecting iris color pixels in the skin colored region using a Bayes model; and
locating eve positions based upon the detected iris color pixels, wherein the step of locating eye positions based upon the detected iris color pixels comprises the steps of;
clustering iris color pixels;
finding the center of each cluster;
dividing the skin colored region into a left-half and a right-half;
locating the most likely left eye position in the left-half region using the summation of squared difference method; and
locating the most likely right eye position in the right-half region using the summation of squared difference method. - View Dependent Claims (11, 12)
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13. A method for detecting human irises and eyes in a digital image comprising the steps of:
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finding a skin color region;
detecting iris color pixels in the skin colored region using a Bayes model; and
locating eye positions based upon the detected iris color pixels;
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;
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 a 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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14. A computer program product for detecting human irises in a digital image, the computer program product comprising a computer readable storage medium having a computer program stored thereon for performing the steps of:
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measuring the red intensity of the pixels in the image;
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
determining whether each pixel is an iris by analyzing the relationship between the probability that the pixel is an iris and probability that the pixel is not an iris. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer program product for detecting human irises and eyes in a digital image, the computer program product comprising a computer readable storage medium having a computer program stored thereon for performing the steps of:
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finding a skin colored region;
detecting iris color pixels in the skin colored region using a Bayes model; and
locating eye positions based upon the detected iris color pixels;
wherein the step of locating eye positions based upon the detected iris color pixels comprises the steps of;
clustering iris color pixels;
finding the center of each cluster;
dividing the skin colored region into a left-half and a right-half;
locating the most likely left eye position in the left-half region using the summation of squared difference method; and
locating the most likely right eye position in the right-half region using the summation of squared difference method. - View Dependent Claims (24, 25, 26)
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Specification