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 - 23 . (canceled)
24 . A method for segmenting components of an eye image for creating data to be used in a system for processing eye image data, said method comprising the steps of:
creating an eye histogram of pixel intensities from a distribution of grey levels of an eye image, said eye histogram having classes, each class corresponding to a component of said eye image;
selecting thresholds in said eye histogram to divide said classes of said histogram; and
creating a thresholded image corresponding to each of said classes.
25 . The method according to claim 24 , wherein said step of selecting thresholds comprises determining maximum variances between said classes.
26 . The method according to claim 25 , wherein said step of determining maximum variances comprises retrieving results for arithmetic calculations from a look-up table.
27 - 49 . (canceled)
50 . A system for segmenting components of an eye image for creating data to be used for processing eye image data, said system comprising:
means for creating an eye histogram of pixel intensities from a distribution of grey levels of an eye image, said eye histogram having classes, each class corresponding to a component of said eye image;
means for selecting thresholds in said eye histogram to divide said classes of said histogram; and
means for creating a thresholded image corresponding to each of said classes.
51 . The system according to claim 50 , wherein said means for selecting thresholds comprises means for determining maximum variances between said classes.
52 . The system according to claim 51 , wherein said means for determining maximum variances comprises means for retrieving results for arithmetic calculations from a look-up table.