초록 ▼

A method for identifying a person from the detected eye image has been developed, the steps of which are: (a) discriminating the image difference by comparing the detected personal eye image with the simple background scene, and compressing the corresponding discriminated eye image; (b) decoding the

A method for identifying a person from the detected eye image has been developed, the steps of which are: (a) discriminating the image difference by comparing the detected personal eye image with the simple background scene, and compressing the corresponding discriminated eye image; (b) decoding the compressed image to the binary coded image by utilizing multi-critical values; (c) defining only the bright region of the binary coded image as the surveying area; (d) generating the histogram for the surveying area; (e) comparing the specified histogram of the surveying area with various pre-stored templates to sort-out the similar facial groups and pick at least one similar eye region among the similar facial groups, and (f) determining the best matched eye appearance through the character analysis of the selected similar eye regions.

대표청구항 ▼

What is claimed is: 1. A method for identifying a person by using a detected eye image, the method comprising steps of: discriminating an image, this is differentiated between a detected personal image and a background scene, compressing the discriminated image to one-seventh of its original size,

What is claimed is: 1. A method for identifying a person by using a detected eye image, the method comprising steps of: discriminating an image, this is differentiated between a detected personal image and a background scene, compressing the discriminated image to one-seventh of its original size, decoding the compressed image into a binary-code image by utilizing multi-critical values, defining a bright portion of the binary-code image for a searching region, generating a histogram for a surveying region, comparing a specified histogram of the searching region with various templates to sort-out a similar facial regions and select at least one similar eye region among the similar facial regions, and determining an eye position, this is closely matched among the selected similar eye regions through a characteristic analysis. 2. The method for identifying a person as claimed in claim 1, wherein said decoding process further comprising sub-steps of: setting a critical value "1" for pixel 10, and the critical value "2" for pixel 30, for decoding the binary-code, analyzing an absolute value of each pixel for the discriminated image, setting an output pixel value "0, " if the pixel value is between the critical value "1", setting the output pixel value "128," if the pixel value is between the critical value "1" and "2", setting the output pixel value "255," if the pixel value is above the critical value "2", re-assigning the pixel value either "0" or "255" for the pixel value "128", according to a result of image analysis of surrounding components, decoding the binary-code for regions having a pixel energy value between the pixel value "0" or "255", and calculating a distance between a starting point and an ending point, which has the pixel value "255" in the searching region, excluding regions where the counted pixel values exceed a preset number and selectively determining the searching region. 3. The method for identifying a person as claimed in claim 1, wherein said comparing process further comprising sub-steps of: selecting a group of candidate face regions, which are containing similar face images, by using various template sizes, eliminating pixels disposed outside boundary, which are not necessary for comparing the face image, which are two rows and two columns of pixels at the periphery of the face image region from the selected face image of the candidate regions, and two by two pixels of leftmost and rightmost of the face image region with respect to the central axis of the selected face image of the candidate regions, selecting a darkest pixel of symmetrical portion between left and right face image, determining the candidate eye position by restoring the original eye image position, calculating a distance between the left and right eyes in the pixel by analyzing the eye position of the candidate, and determining an optimum eye position by proportionally adjusting the distance of the left and right eye image regions. 4. The method for identifying a person as claimed in claim 1, wherein said determining process further comprising sub-steps of: calculating an average value of brightness for a candidate eye region having 5��5 pixels and mound eye center position, evaluating the average value of the brightness for a surrounding regions, having 5 pixels apart around the eye center position of 5��5 block, analyzing the average value of the brightness for all other candidate eye regions, calculating a light scattering value at each row and column of the candidate eye regions, estimating an overall average light scattering value, analyzing the light scattering values for all other candidate eye regions, determining an optimum eye position among the candidate eye regions based on the Local Feature Analysis (LFA), which has a resulting value of the brightness less than average value of the surrounding blocks and the highest scattering value.