System and method for non-cooperative iris image acquisition
First Claim
1. A method for segmenting an eye image to identify portions of the eye image containing iris pattern data comprising:
- receiving a frame of eye image data;
convolving the eye image data with a predetermined matrix of coefficients to produce filtered eye image data, the predetermined matrix of coefficients being configured to produce peak points in the filtered eye image data at positions where glare is not present in the eye image data;
comparing the filtered eye image data to a threshold;
identifying a number of peak points in the filtered eye image data, the identified peak points in the filtered eye image data corresponding to positions of no glare in the eye image data; and
segmenting the eye image data to identify portions of the frame containing iris pattern data only in response to at least one peak point being present in the filtered eye image data.
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Abstract
A method segments iris images from eye image data captured from non-cooperative subjects. The method includes receiving a frame of eye image data, and determining whether a pupil exists in the image by detecting glare areas in the image. Upon finding a pupil, subsequent images are processed with reference to the pupil location and a radius is calculated for the pupil. A k means clustering method and principal component analysis are used to locate pupil boundary points, which are fitted to a conic. Using the pupil boundary, an angular derivative is computed for each frame having a pupil and iris boundary points are fitted to a conic to identify an iris region between the iris boundary and the pupil boundary. Noise data are then removed from the iris region to generate an iris segment. A method for evaluating iris frame quality and iris image segmentation quality is also disclosed.
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Citations
24 Claims
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1. A method for segmenting an eye image to identify portions of the eye image containing iris pattern data comprising:
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receiving a frame of eye image data; convolving the eye image data with a predetermined matrix of coefficients to produce filtered eye image data, the predetermined matrix of coefficients being configured to produce peak points in the filtered eye image data at positions where glare is not present in the eye image data; comparing the filtered eye image data to a threshold; identifying a number of peak points in the filtered eye image data, the identified peak points in the filtered eye image data corresponding to positions of no glare in the eye image data; and segmenting the eye image data to identify portions of the frame containing iris pattern data only in response to at least one peak point being present in the filtered eye image data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 24)
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13. A method for evaluating quality in a non-cooperative iris image identification system comprising:
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identifying regions of glare and motion blur in each frame of a plurality of frames in a video sequence of eye images, the detection of the regions of glare and motion blur further comprising; applying a Sobel filter to image data in a frame; identifying bright area pixels corresponding to the Sobel filtered image data exceeding a first threshold; identifying gray area pixels in the Sobel filtered image data corresponding to the bright area pixels, the gray area pixels being less than a second threshold, the second threshold being greater than the first threshold; identifying a quality score of the frame with reference to a first number of identified bright area pixels and a second number of identified gray area pixels; discarding frames from the video sequence for segmentation processing in response to the identified number of gray area pixels being greater than a predetermined percentage of the bright area pixels for the frame; generating a plurality of iris image segments with reference to frames of the video sequence for which the quality score is greater than the threshold; generating a segmentation score for the plurality of iris segments, the generation of the segmentation score further comprising; generating a pupil boundary segmentation score; generating an iris boundary segmentation score normalizing the pupil boundary segmentation score and the iris boundary segmentation score to produce the segmentation score; and generating an overall score for an iris pattern in the frame used to identify a person with reference to a combination of the generated segmentation score and the identified quality score. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
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Specification