Distance iris recognition

• US 8,285,005 B2
• Filed: 08/11/2009
• Issued: 10/09/2012
• Est. Priority Date: 01/26/2005
• Status: Active Grant

• Current Assignee: Gentex Corporation
• Original Assignee: Honeywell International Inc.
• Inventors: Hamza, Rida M.
• Primary Examiner(s): Le, Brian
• Application Number:
  US12/539,278
• Publication Number:
  US 20100002913A1
• Time in Patent Office: 1,155 Days
• Field of Search: 382/103, 382/115, 382/117, 382/118, 382/154, 356/4.03, 356/4.04, 356/4.05, 356/4.06, 356/4.07, 356/18, 356/492, 356/493, 356/496, 356/498
• US Class Current: 382/117
• CPC Class Codes: G06V 40/18 Eye characteristics, e.g. of the iris
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction

Abstract

A system for one dimensional segmentation of an iris of an eye into a map of the iris and classification of the map into unaffected areas and affected areas. Also, the system may provide for regular shape fitting of the areas for normalization and identifying the unaffected areas as symmetric segments. Further, the system may assign weights to the unaffected areas and the affected areas of the map of the iris and an enrolled map of an iris and their corresponding bins for matching purposes.

Claims

1. A method of iris capture, comprising:

one dimensional segmentation of an iris into a map of the iris; and

classification of the map into unaffected areas and affected areas;

assigning weights to the unaffected areas and the affected areas of the map of the iris and an enrolled map of an iris; and

wherein;

higher weights are attributed to unaffected areas of the map of the iris and the enrolled map of an iris;

lower weights are attributed to affected areas of the map of the iris and the enrolled map of an iris; and

weights between the higher weights and the lower weights are attributed to unaffected and affected areas of the map of the iris and the enrolled map of an iris.

2. The method of claim 1, further comprising regular shape fitting of the areas for normalization.

3. The method of claim 2, further comprising identifying the unaffected areas as symmetric areas.

4. The method of claim 3, further comprising calibrating the affected areas.

5. The method of claim 4, further comprising clustering the affected areas.

6. The method of claim 5, further comprising assigning a weighting to the unaffected areas and the affected areas.

7. The method of claim 6, wherein the affected areas are affected by one or more eyelids and/or one or more eyelashes.