Iris data extraction
A process for extracting iris data for biometric identification includes a thresholding method where the thresholds are selected according to a nonparametric approach that considers the grey scale and does not require classifying pixels as edge or non-edge pixels. An eye image is first acquired, where the eye image has component images including an iris image with an inner boundary and an outer boundary. The eye image has a distribution of grey levels. Component images, such as an iris image or a pupil image, from the eye image are segmented according to the distribution of grey levels. The inner boundary and outer boundary of the iris image are determined from the component images. The iris image within the inner boundary and outer boundary is processed for biometric identification. The component images may be segmented by creating an eye histogram of pixel intensities from the distribution of grey levels.
1. A computer-implemented method for segmenting components of an eye image for creating data to be used in a system for processing eye image data, the method executed by one or more computing systems and comprising:
creating, by the one or more computing systems, an eye histogram of pixel intensities from a distribution of grey levels of an eye image, the eye histogram having two or more classes, each class corresponding to a component of the eye image;
selecting, by the one or more computing systems, thresholds in the eye histogram to divide the classes of the histogram;
creating, by the one or more computing systems, threshold images, each of the threshold images corresponding to one of the two or more classes; and
removing, by the one or more computing systems, a portion of the eye image based on one or more of the threshold images.
2. The method of claim 1 , wherein one of the threshold images corresponds to an eyelash image, and removing a portion of the eye image based on one or more of the threshold images comprises removing a portion of the eye image based on the threshold image corresponding to the eyelash image.
3. The method of claim 1 , wherein one of the threshold images corresponds to a specular reflection image, and removing a portion of the eye image based on one or more of the threshold images comprises removing a portion of the eye image based on the threshold image corresponding to the specular reflection image.
4. The method of claim 1 , wherein selecting thresholds in the eye histogram comprises determining maximum between-class variances between the two or more classes.
5. The method of claim 4 , wherein determining the maximum between-class variance between the two or more the classes comprises retrieving results for arithmetic calculations from a lookup table.
6. A system comprising:
one or more processors; and
a computer-readable medium coupled to at least one of the one or more processors and having instructions stored thereon which, when executed by the at least one of the one or more processors, causes at least one of the one or more processors to perform operations comprising:
creating an eye histogram of pixel intensities from a distribution of grey levels of an eye image, the eye histogram having two or more classes, each class corresponding to a component of the eye image;
selecting thresholds in the eye histogram to divide the classes of the histogram;
creating threshold images, each of the threshold images corresponding to one of the two or more classes; and
removing a portion of the eye image based on one or more of the threshold images.
7. The system of claim 6 , wherein one of the threshold images corresponds to an eyelash image, and removing a portion of the eye image based on one or more of the threshold images comprises removing a portion of the eye image based on the threshold image corresponding to the eyelash image.
8. The system of claim 6 , wherein one of the threshold images corresponds to a specular reflection image, and removing a portion of the eye image based on one or more of the threshold images comprises removing a portion of the eye image based on the threshold image corresponding to the specular reflection image.
9. The system of claim 6 , wherein selecting thresholds in the eye histogram comprises determining maximum between-class variances between the two or more classes.
10. The system of claim 9 , wherein determining the maximum between-class variance between the two or more the classes comprises retrieving results for arithmetic calculations from a lookup table.