IRIS RECOGNITION SYSTEM USING QUALITY METRICS

• US 20100142765A1
• Filed: 12/05/2008
• Published: 06/10/2010
• Est. Priority Date: 12/05/2008
• Status: Active Grant

• Current Assignee: Gentex Corporation
• Original Assignee: Honeywell International Inc.
• Inventors: Hamza, Rida
• Granted Patent: US 8,280,119 B2
• Time in Patent Office: Days
• US Class Current: 382/117
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern
• CPC Class Codes: G06V 40/18 Eye characteristics, e.g. of the iris

Abstract

A system for iris recognition using a set of quality metrics, which may include eye image validation, blur assessment, offset, gazing, obscuration, visibility, and the like. These metrics may be established as quantitative measures which can automatically assess the quality of eye images before they are processed for recognition purposes. Quadrant iris analysis, histograms, map processing enhancements, and multi-band analysis may be used in aiding in the iris recognition approach.

Claims

1. A method for iris recognition comprising:

locating an eye;

obtaining an image of the eye; and

assessing the image of the eye with a set of image quality metrics.

2. The method of claim 1, further comprising determining a quality score of the image of the eye with the set of image quality metrics.

3. The method of claim 2, further comprising:

determining a matching score of the image of the eye based on matches of the image of the eye with other images of the eye; and

establishing an evaluation score based on the quality score and the matching score.

4. The method of claim 1, wherein the set of image quality metrics comprises:

a validity measurement of the image of the eye; and

a blur measurement of the image of the eye.

5. The method claim 4, wherein the set of image quality metrics further comprise:

an offset measurement of the eye in the image of the eye; and

a gaze measurement of the eye in the image of the eye.

6. The method of claim 5, further comprising:

segmenting the iris in the image of the eye; and

wherein a calibration of the segmenting of the iris determined by the offset and gaze measurements.

7. The method of claim 6, wherein:

if the offset and gaze measurements indicate offset or gaze of the eye in the image of the eye, then the segmenting of the iris is based on no circular calibration; and

if the offset and gaze measurements indicate no offset or gaze of the eye, then the segmenting of the iris is base on circular calibration.

8. The method of claim 6, wherein the set of image quality metrics further comprises:

an obscuration measurement of the eye in the image of the eye; and

a visibility measurement of the iris of the eye.

9. The method of claim 8, wherein:

if the obscuration measurement of the eye reveals the eye not to be obscured, then a map of an iris of the eye is extracted; and

a visibility measurement of the iris in the map of the iris is made.

10. The method of claim 9, further comprising:

coding the iris in a single band code and/or multi-band code with associated weights based on the quality scores in particular blur; and

matching the single band code and/or multi-band code with other codes of the iris.

11. The method of claim 6, wherein the quadrant iris analysis comprises:

finding a first limit in a first quadrant toward a second quadrant, starting from a sclera in a first direction;

finding a second limit in newly defined second quadrant based on the first limit, starting from the sclera at the horizontal axis in a second direction opposite the first direction;

applying an interpolation/mask test to the first and second quadrants;

finding limits in a third quadrant in a direction toward a fourth quadrant;

finding limits in a newly defined fourth quadrant in an opposite direction; and

applying an interpolation/mask test to the third and fourth quadrants.

12. An iris recognition system comprising:

an eye image source;

a quality metric mechanism connected to the eye image source; and

wherein the quality metric mechanism is for assessing eye images according to a set of quality metrics.

13. The system of claim 12, wherein the set of quality metrics comprises:

validation measurement;

blur measurement;

gazing measurement;

offset measurement;

obscuration measurement; and

/or

visibility measurement.

14. The system of claim 12, wherein the quality metric mechanism can automatically assess eye images from the eye image source.

15. The system of claim 13, further comprising:

a matching mechanism connected to the quality metric mechanism; and

wherein the matching mechanism is for providing a matching measurement of an eye image.

16. The system of claim 15, wherein:

the quality metric mechanism is for providing a quality score of an eye image;

the matching mechanism can provide a matching score for an eye image; and

the quality score and the match score are a basis for evaluating an eye image.

17. A system for iris recognition using quality metrics, comprising:

an eye image source;

a quality metric mechanism connected to the eye image source; and

an iris map extractor connected to the quality metric mechanism.

18. The system of claim 17, wherein an iris map is processed to eliminate outliers in the map.

19. The system of claim 18, wherein clustering pixel intensities of the map is based on their distributed histogram by selectively choosing limits of an iris as represented in the iris map using associated valleys on the left and right of an iris pixel cluster.

20. The system of claim 17, wherein:

a limited range is imposed to preserve a certain percentage of pixel elements of a prominent pixel cluster which likely represents the iris in the map; and

an element beyond the limited range or the estimated thresholds of the pixel cluster represents noise subject to removal.