Vision-based operating method and system

• US 7,369,685 B2
• Filed: 04/05/2002
• Issued: 05/06/2008
• Est. Priority Date: 04/05/2002
• Status: Expired due to Term

• Current Assignee: Morphotrust USA Incorporated (Advent International Corporation)
• Original Assignee: Identix, Inc. (Advent International Corporation)
• Inventors: DeLean, Bruno C.
• Primary Examiner(s): Bella; Matthew C.
• Assistant Examiner(s): Hung; Yubin
• Application Number:
  US10/116,839
• Publication Number:
  US 20030190076A1
• Time in Patent Office: 2,223 Days
• Field of Search: 382115-118, 382/124, 382/209, 382/305, 707/6
• US Class Current: 382/118
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06V 10/757 Matching configurations of points or features
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A vision-based operating system is provided in which a computer-based system accepts visual inputs and takes appropriate actions in a wide variety of environments. One environment is a security environment in which the vision-based operating system acquires an image of a face and determines, with an arbitrarily small probability of a false positive, whether the face is of a person who is authorized to enter the environment. The method employed may include acquiring a digital image for comparison to a reference image and identifying a group of pixels in the acquired image. A pixel group in the reference image that is the best match for the pixel group in the acquired image is selected. These steps are repeated for at least one more group of pixels. The relative locations of the selected pixel groups in the reference images and the pixel groups in the acquired image are compared, and the probability that the relative locations occurred randomly is determined.

Claims

1. A method of determining whether an acquired image matches a reference image, comprising:

acquiring a digital image for comparison to a reference image;

identifying a group of pixels in the acquired image;

selecting the pixel group in the reference image that is the best match for the pixel group in the acquired image;

repeating the preceding steps for at least one more group of pixels;

comparing the relative locations of the selected pixel groups in the reference image and the pixel groups in the acquired image; and

determining the probability that the relative locations occurred randomly.

2. A method of claim 1, further comprising:

comparing the probability of a random match to a threshold probability for concluding that the images match.

3. A method of claim 2, wherein the threshold probability is set to reduce the probability of a false conclusion to an arbitrarily low number.

4. A method of claim 1,

wherein finding the closest pixel group uses a least squares calculation of the differences between the pixel groups in the reference image and the pixel groups in the acquired image.

5. A method of claim 1, further comprising:

upon acquiring the reference image and the acquired image, filtering the images prior to completing the additional steps.

6. A method of claim 5, wherein the filtering comprises:

determining the luminance of a pixel based on an average of values of neighboring pixels;

comparing the value of the luminance of the pixel to a threshold value;

setting the filtered value of the pixel to zero if the luminance is below the threshold; and

setting the filtered value of the pixel to one if the luminance is equal to or greater than the threshold value.

7. A method of claim 6, further comprising:

normalizing the acquired image and the reference image by locating known features in a predetermined location.

8. A method of claim 1, wherein acquiring the image is accomplished by a vision processing system having a vision-based operating system.

9. A method of claim 1, further comprising:

comparing the acquired image to more than one reference image.

10. A method of claim 1, wherein comparing the relative locations of the pixel groups of the acquired image and the pixel groups of the reference image comprises:

defining a first vector between the first pixel group of the acquired image and a second pixel group of the acquired image;

defining a second vector between the first pixel group of the reference image and the second pixel group of the reference image; and

calculating an absolute value of the difference between the first vector and the second vector.

11. A method of claim 10, further comprising:

repeating the calculation for additional pixel groups.

12. A method of claim 1, further comprising:

comparing the probability of a random match to a threshold probability for concluding that the images match;

repeating the steps for a different reference image; and

allowing unlimited number of mismatches before allowing a match to one of the reference images.

13. A method of claim 1, further comprising:

upon determining a match, adding the acquired image to a database of reference images to facilitate future matches.

14. The method of claim 1, wherein the acquired digital image corresponds to a patch of skin.

15. A method of analyzing an image comprising:

acquiring a first surface area of a surface location of an image of a person to be analyzed;

retrieving a second surface area of the same surface location of a reference image;

identifying a first group of pixels in said first surface area;

selecting a second group of pixels from said second surface area that is a best match for said first group of pixels;

repeating the above steps for at least one more group of pixels in said first and second surface areas;

comparing the relative locations of said pixel groups in said first and second surface areas;

determining a probability that the relative locations occurred randomly; and

calculating a score representing a likelihood that said first and second surface areas are from the same person.

16. A system for determining whether an acquired image matches a reference image, comprising:

a camera for acquiring a digital image for comparison to a reference image;

an identification module for identifying a group of pixels in the acquired image;

a selection module for selecting the pixel group in the reference image that is the best match for the pixel group in the acquired image;

a comparison module for repeating the preceding steps for at least one more group of pixels and for comparing the relative locations of the selected pixel groups in the reference image and the pixel groups in the acquired image; and

a calculator for determining the probability that the relative locations occurred randomly.

17. A system of claim 16, further comprising:

a match module for comparing the probability of a random match to a threshold probability for concluding that the images match.

18. A system of claim 17, wherein the threshold probability is set to reduce the probability of a false conclusion to an arbitrarily low number.

19. A system of claim 16, wherein finding the closest pixel group uses a least squares calculation of the differences between the pixel groups in the reference image and the pixel groups in the acquired image.

20. A system of claim 16, further comprising:

a filter for filtering the images prior to completing the additional steps.

21. A system of claim 20, wherein the filtering comprises determining the luminance of a pixel based on an average of values of neighboring pixels, comparing the value of the luminance of the pixel to a threshold value, setting the filtered value of the pixel to zero if the luminance is below the threshold and setting the filtered value of the pixel to one if the luminance is equal to or greater than the threshold value.

22. A system of claim 21, further comprising:

a normalization module for normalizing the acquired image and the reference image by locating known features in a predetermined location.

23. A system of claim 16, wherein acquiring the image is accomplished by a vision processing system having a vision-based operating system.

24. A system of claim 16, further comprising:

a comparison module for comparing the acquired image to more than one reference image.

25. A system of claim 16, wherein comparing the relative positions of the pixel groups of the acquired image and the pixel groups of the reference image comprises:

defining a first vector between the first pixel group of the acquired image and a second pixel group of the acquired image;

defining a second vector between the first pixel group of the reference image and the second pixel group of the reference image; and

calculating an absolute value of the difference between the first vector and the second vector.

26. A system of claim 16, wherein:

the calculator compares the probability of a random match to a threshold probability for concluding that the images match; and

further including;

a reference image retrieval module for repeating the steps for a different reference image, wherein the system allows an unlimited number of mismatches before allowing a match to one of the reference images.

27. A system of claim 16, further comprising:

an image storage module for, upon determining a match, adding the acquired image to a database of reference images to facilitate future matches.