IRIS AND OCULAR RECOGNITION SYSTEM USING TRACE TRANSFORMS

• US 20100316263A1
• Filed: 06/11/2010
• Published: 12/16/2010
• Est. Priority Date: 06/15/2009
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Hamza, Rida
• Granted Patent: US 8,472,681 B2
• Time in Patent Office: Days
• US Class Current: 382/117
• CPC Class Codes: G06V 10/42 Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 10/48 by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
• CPC Class Codes: G06V 40/197 Matching; Classification

Abstract

An iris and ocular recognition system using trace transforms. The system may acquire an eye image and provide it to an image quality metrics determination module for a quality evaluation to indicate whether the image goes to an iris recognition module and/or a trace transform module. The trace transform may also be used as a pre-filtering mechanism to determine a small database from bigger datasets. If the quality evaluation reaches a predefined quality measure, the image may be passed immediately to the iris recognition module. If not, the image may go to the trace transform module. If the quality evaluation is too poor for the latter, the image may be rejected subject to rehabilitation or reacquisition. A processed image from the iris recognition module may result in an only best match. A processed image from the trace transform module may be lined up instead with the most probable matches.

Claims

1. A standoff ocular recognition system comprising:

an eye image acquisition module;

an image quality evaluation section connected to the eye image acquisition module;

an eye recognition section connected to the image quality evaluation section; and

a matcher module connected to the eye recognition section; and

wherein the eye recognition section comprises a trace transform module for converting eye images into trace space.

2. The system of claim 1, wherein the eye recognition section further comprises an iris recognition module.

3. The system of claim 1, wherein the trace transform module comprises:

an ocular trace transform sub-module connected to the image quality evaluation section; and

an iris trace transform sub-module connected to the image quality evaluation section.

4. The system of claim 2, wherein the image quality evaluation section comprises:

an eye image quality metrics determination module connected to the eye image acquisition module; and

an eye image router module connected to the eye image quality metrics determination module and the eye recognition section.

5. The system of claim 3, further comprising a results module connected to the matcher module.

6. The system of claim 5, wherein the matcher module comprises:

a holistic eye matcher connected to the ocular recognition module; and

an iris matcher connected to the iris trace transform sub-module.

7. The system of claim 6, wherein the results module comprises:

a holistic eye results sub-module connected to the holistic eye matcher; and

an iris results sub-module connected to the iris matcher.

8. The system of claim 7, wherein:

the holistic eye matcher is connected to a database of trace model parameters associated with individuals; and

the iris matcher is connected to a database of iris maps associated with individuals.

9. The system of claim 8, wherein:

the holistic eye matcher provides to the holistic results sub-module most probable matches rather than an only best match; and

the most probable matches are determined according to a similarity measure between the trace model parameters of eye images provided to the ocular trace transform sub-module and trace model parameters in the database of trace model parameters associated with individuals.

10. The system of claim 9, wherein the iris matcher provides an only best match.

11. The system of claim 3, wherein:

the ocular trace transform sub-module comprises information about features of a structure of the eye and skin texture proximate to the eye; and

the iris trace transform sub-module comprises information about a pattern of the iris.

12. The system of claim 2, wherein the iris recognition module comprises a polar segmentation approach for extracting a map of an iris from an eye image provided from the image quality evaluation section.

13. The system of claim 2, wherein the iris recognition module is connected to an enroller module for enrolling maps of the irises and identities of individuals associated with the maps of irises, in a database.

14. The system of claim 4, wherein:

the eye image router module provides eye images, deemed not to meet a threshold of quality metrics according to the eye image quality metrics determination module, primarily to the trace transform module;

the eye image router module provides eye images, deemed to meet the threshold of quality metrics according to the eye image quality metrics determination module, primarily to the iris recognition module; and

the eye image router module can provide eye images approximately simultaneously to the trace transform module and the iris recognition module, with an outcome weighed based on iris quality.

15. The system of claim 3, wherein:

the ocular trace transform sub-module is connected to an enroller module for enrolling trace transforms of ocular images and identities of individuals associated with the trace transforms, in a database; and

the iris trace transform sub-module is connected to an enroller module for enrolling trace transforms of iris images and identities of individuals associated with the trace transforms, in a database.

16. The system of claim 1, wherein in the trace transform module, prior to a trace transformation, a normalization based on intersection points of two estimated eyelid parabolas, one parabola being fit to an edge of an upper eyelid and another parabola being fit to an edge of a lower lid, has the intersection points as reference points for the normalization of various templates.

17. A method for ocular recognition, comprising:

acquiring an eye image;

evaluating the eye image relative to quality;

converting eye images having poor quality to trace transforms;

matching ocular trace transforms with ocular trace transforms, associated with identities of individuals, in a database; and

wherein;

an eye image has poor quality if the eye image is evaluated not to meet a predefined threshold of quality metrics; and

the matching ocular trace transforms of eye images is based on a similarity measure relative to the ocular trace transforms in the database.

18. The method of claim 17, wherein the matching based on the similarity measure generates a group of most probable matches instead of one best match.

19. The method of claim 17, further comprising:

processing eye images having good quality into iris images for iris recognition; and

matching the iris images with iris images, associated with identities of individuals, in a database; and

wherein;

an image has good quality if the eye image is evaluated to meet a predefined threshold of quality metrics; and

a matching of iris images generates a one best match.

20. An approach for standoff ocular recognition, comprising:

acquisition of an eye image;

determination of quality metrics of the eye image;

trace transformation of an eye image, having quality metrics below a certain threshold, into ocular signature images; and

a matching of the ocular signature images with ocular signature images associated with identities of individuals in a database, which generates a number of most probable matches; and

wherein an eye image having a quality below a certain threshold is deemed to provide an iris print too obscured to be a reliable matcher for selecting one best match among iris prints in the database.