ADAPTIVE IRIS MATCHING USING DATABASE INDEXING

• US 20100315500A1
• Filed: 06/11/2010
• Published: 12/16/2010
• Est. Priority Date: 06/15/2009
• Status: Active Grant

• Current Assignee: Gentex Corporation
• Original Assignee: Honeywell International Inc.
• Inventors: Hamza, Rida, Cohen, Isaac, Whillock, Rand
• Granted Patent: US 8,630,464 B2
• Time in Patent Office: Days
• US Class Current: 348/78
• CPC Class Codes: G06V 10/98 Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction

Abstract

An adaptive iris matching approach for processing images of irises having a quality not sufficient for conventional non-adaptive matching approaches. Visible regions in a radial direction on an iris, without segmenting a circumferential of the iris, may be processed. A boundary of the visible region of the iris may be estimated. An iris map may be constructed with the non-visible portion of the iris masked. The iris map may be at least partially encoded. Partial codes of the iris map may be extracted to index at least portions of a database containing iris information. Irises may be retrieved from the database with an iris code as a query.

Claims

1. A standoff iris recognition system comprising:

an iris image acquisition module;

an image quality evaluator connected to the iris image acquisition module; and

an adaptive iris matching module connected to the image quality evaluator and the eye image acquisition module; and

wherein the image quality evaluator determines if an eye image from the iris image acquisition module has a quality which is too low for a non-adaptive iris matching module and is sufficiently high enough for the adaptive iris matching module.

2. The system of claim 1, wherein the non-adaptive iris matching module processes an iris image having sufficient quality so that a best match for the iris image can be generated from a database of iris images.

3. The system of claim 2, wherein the adaptive iris matching module processes an iris image having low but sufficient quality so that a group of most probable matches for the iris image instead of one best match can be generated from a database of iris images.

4. The system of claim 3, wherein the image quality evaluator determines whether an iris image has sufficient quality for successful processing by the non-adaptive iris matching module or not sufficient quality for successful processing by the non-adaptive iris matching module but has sufficient quality for processing by the adaptive iris matching module, or not sufficient quality for processing by the non-adaptive iris matching module or by the adaptive iris matching module.

5. The system of claim 4, wherein:

if an iris image has not sufficient quality for processing by the non-adaptive iris matching module or by the adaptive iris matching module, then the image is rehabilitated, if possible, for processing by the non-adaptive iris matching module or by the adaptive iris matching module; and

if the iris image cannot be rehabilitated, then the iris image is rejected.

6. The system of claim 1, wherein the image quality evaluator determines a quality of an iris image relative to blur, obscuration, standoff angle, and/or iris visibility of the iris image.

7. The system of claim 1, wherein the adaptive iris matching module is for segmenting a visible portion of an iris image, partial encoding, database indexing, and/or adaptive matching relative to the visible portion of the iris image.

8. The system of claim 7, wherein the adaptive iris matching module is further for normalizing data points of an iris portion along a visible radial at an angle segment of an iris image.

9. The system of claim 7, wherein the adaptive iris matching module is further for encoding a partial visible iris map signature of an iris image.

10. The system of claim 7, wherein the database indexing comprises:

providing a database having templates; and

grouping the database into a plurality of sub-databases; and

wherein;

each template in the database has a first number of bits based on angular indexing;

each sub-database of the plurality of sub-databases represents virtually all templates in the database; and

/or

each template in a sub-database has a second number of bits based on a hierarchical structure.

11. The system of claim 10, wherein the database indexing further comprises:

providing a subset of template barcodes for possible matching; and

/or

selecting a second number of bits of a barcode that corresponds to the second number of bits of each template.

12. The system of claim 11, wherein the adaptive iris matching module is further for normalizing a number of data points from an obscured iris portion of an iris image to match one of the templates.

13. The system of claim 12, wherein the adaptive iris matching module is further for encoding a partial obscured iris map signature.

14. The system of claim 13, wherein the adaptive iris matching module is further for comparing iris barcodes to a previously generated reference iris signature.

15. An approach comprising:

segmenting a visible region of an iris, with a model providing a reasonable first order estimate of a border segment;

extracting associated partial codes of a barcode of the iris to index a smaller population of a whole population; and

recovering noisy information in an obscured area by adapting a fitting model that provides best matching bits within the visible region of the iris.

16. The approach of claim 15, wherein iris dyadic variations are captured by formulating a dynamic encoder that takes into account varying edge points.

17. The approach of claim 16, wherein the encoding scheme is modified to make use of a dynamic wavelet function that varies as a function for an angular variable.

18. The approach of claim 17, wherein:

the dynamic wavelet function is a smooth continuous function of the angular variable; and

an output of the dynamic wavelet function is enabled at each angle using virtually all possible values for a best match.

19. The approach of claim 18, wherein concatenated matched bits can determine an overall match score between a probe and a query barcode.

20. A method for adaptive iris matching, comprising:

acquiring an image of an iris;

evaluating quality of the image;

processing a visible region of the iris in the image on a radial direction without circumferential of iris in the image, if the quality is too low for non-adaptive iris matching but high enough for adaptive iris matching;

estimating a boundary for the visible region of the iris in the image;

constructing an iris map using a rubber sheet model;

masking a non-visible region of the iris;

encoding the iris map for at least partial comparisons between templates;

extracting associated partial codes of an iris code to index a portion of a database; and

retrieving irises from the database with the iris code as a query.