Image recognition

• US 20070122007A1
• Filed: 10/11/2004
• Published: 05/31/2007
• Est. Priority Date: 10/09/2003
• Status: Active Grant

• Current Assignee: University of York
• Original Assignee: University of York
• Inventors: Austin, James, Heseltine, Thomas, Pears, Nicholas
• Granted Patent: US 7,689,043 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions

Abstract

A camera A produces a 3D natural image B which is re-oriented and repositioned at N to predetermined parameters. Different processes C, D, E extract different features from the image to provide different processed images. The data space occupied by the processed images is reduced, for example by Principle Component Analysis, at F, G, H and the reduced processed images are combined at O to provide an image key I representative of the image B. The image key I is compared at J with stored image keys L of known images, and the output comparison is sorted at K to produce a final list M of potential matches. In a verification process, just a single image key L may be compared. The processed images at C, D, E may alternatively be combined prior to a single subspace reduction and/or optimisation method. 2D data may be combined with or used instead of the 3D data.

Claims

1. A method of recognising an image, comprising the steps of:

a. processing the image to provide an image set containing a plurality of different processed images;

b. combining the processed images in the image set;

c. transforming the data space occupied by the processed images in the image set;

d. generating, from the image-set represented in the transformed data space, an image key representative of the image; and

e. comparing the image key with at least one previously stored image key of a known image.

2. A method according to claim 1, wherein step a. includes extracting image features including at least one of edges, lines, wavelets, gradient components, curvature components and colour components.

3. A method according to claim 1, wherein step b. is carried out prior to step c.

4. A method according to claim 1, wherein step c. is carried out prior to step b.

5. A method according to claim 1, wherein step e. comprises comparing the image key with just one previously stored image key, to verify the identity of the image.

6. A method according to claim 1, wherein step e. comprises comparing the image key with a plurality of previously stored image keys, to identify the image.

7. A method according to claim 6, comprising the further step of sorting the results of the comparison in step e. to produce a list of potential matches with previously stored image keys.

8. A method according to claim 6, wherein step e. is carried out using a Euclidean distance metric (the L2 norm), mahalanobis distance metric or a cosine distance metric.

9. A method according to claim 1, including the step prior to step a. of rotating and/or positioning the image to a predetermined orientation and/or position and/or depth normalisation.

10. A method according to claim 1, including a step prior to step b. of normalising data prior to combination.

11. A method according to claim 1, wherein said image is obtained from a camera.

12. A method according to claim 1, wherein said image comprises 3D data.

13. A method according to claim 1, wherein said image comprises 2D data.

14. A method according to claims 12 wherein said image comprises a registered 2D-3D image pair.

15. A method according to claim 1, wherein step c. is carried out by a Principal Component Analysis method.

16. A method according to claim 1, wherein step c. is carried out by Fisher'"'"'s Linear Discriminant Analysis method.

17. A method according to claim 1, wherein said image is an image of a face.

18. A method according to claim 1, wherein said image is an image of a human face.

19. A method according to claim 1, wherein said image is a natural image.

20. A method according to claim 1, wherein said image set includes the original image.

23. Apparatus for recognising an image, the apparatus comprising, and arranged to perform a method according to claim 1:

a. processing means arranged to process the image to provide a plurality of different processed images.

b. combining means arranged to combine the processed images;

c. reducing means arranged to reduce the data space occupied by the processed images;

d. generating means arranged to generate from the combined and reduced processed images an image key representative of the image; and

e. comparison means arranged to compare the image key with at least one previously stored image key of a known image.

21. (canceled)

22. Apparatus for recognising an image, the apparatus comprising:

a. processing means arranged to process the image to provide a plurality of different processed images.

b. combining means arranged to combine the processed images;

c. reducing means arranged to reduce the data space occupied by the processed images;

d. generating means arranged to generate from the combined and reduced processed images an image key representative of the image; and

e. comparison means arranged to compare the image key with at least one previously stored image key of a known image.

24. (canceled)

25. A method of recognising a three-dimensional image, comprising the steps of:

a. transforming the data space occupied by the image using Fisher'"'"'s Linear Discriminant Analysis;

b. generating, from the transformed data space, an image key representative of the image; and

c. comparing the image key with at least one previously stored image key of a known image.

26. Apparatus for recognising a three-dimensional image, the apparatus comprising:

a. means for transforming the data space occupied by the image using Fisher'"'"'s Linear Discriminant Analysis;

b. means for generating, from the transformed data space, an image key representative of the image; and

c. means for comparing the image key with at least one previously stored image key of a known image.