ADVANCES IN EXTENDING THE AAM TECHNIQUES FROM GRAYSCALE TO COLOR IMAGES

• US 20080013798A1
• Filed: 06/12/2007
• Published: 01/17/2008
• Est. Priority Date: 06/12/2006
• Status: Active Grant

• Current Assignee: FotoNation Limited (Adeia Inc.), National University of Ireland Galway
• Original Assignee: FotoNation Vision Limited (Adeia Inc.)
• Inventors: Corcoran, Peter, Ionita, Mircea
• Granted Patent: US 7,965,875 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06V 10/7557 based on appearance, e.g. active appearance models [AAM]
• CPC Class Codes: G06V 40/162 using pixel segmentation or colour matching

Abstract

A face detection and/or detection method includes acquiring a digital color image. An active appearance model (AAM) is applied including an interchannel-decorrelated color space. One or more parameters of the model are matched to the image. Face detection results based on the matching and/or different results incorporating the face detection result are communicated.

Claims

1. A method of detecting faces in a digital image, comprising:

(a) acquiring a digital color image;

(b) applying an active appearance model (AAM) including an interchannel-decorrelated color space;

(c) matching one or more parameters of the model to the image; and

(d) communicating a face detection result based on the matching or a different processing result incorporating said face detection result, or both.

2. The method of claim 1, further comprising converting RGB data to I₁I₂I₃ color space.

3. The method of claim 2, wherein the converting comprises linear conversion.

4. The method of claim 2, further comprising representing texture with the I₁I₂I₃ color space.

5. The method of claim 4, further comprising aligning the texture on separate channels.

6. The method of claim 4, further comprising performing operations on the texture data on each channel separately.

7. The method of claim 1, wherein said interchannel-decorrleated color space comprises at least three channels including a luminance channel and two chromatic channels.

8. The method of claim 1, wherein the AAM comprises an application of principal components analysis (PCA).

9. The method of claim 8, wherein said PCA comprises eigen-analysis of dispersions of shape, texture and appearance.

10. The method of claim 8, wherein the AAM further comprises an application of generalized procrustes analysis (GPA) including aligning shapes.

11. The method of claim 10, wherein the AAM further comprises a model of shape variability including an application of PCA on a set of shape vectors.

12. The method of claim 11, wherein the AAM further comprises a normalization of objects within the image with respect to shape.

13. The method of claim 12, wherein the AAM further comprises generation of a texture model including sampling intensity information from each shape-free image to form a set of texture vectors.

14. The method of claim 13, wherein the generation of the texture model comprising normalization of the set of texture vectors and application of PCA on the normalized texture vectors.

15. The method of claim 14, wherein the applying comprises retaining only the first one or two of the aligned texture vectors

16. The method of claim 14, wherein the AAM further comprises generation of a combined appearance model including a combined vector from weighted shape parameters concatenated to texture parameters, and application of PCA to the combined vector.

17. The method of claim 1, wherein the matching comprising a regression approach.

18. The method of claim 1, wherein the matching comprises finding model parameters or pose parameters or both.

19. The method of claim 18, wherein the pose parameters comprise translation, scale or rotation, or combinations thereof.

20. The method of claim 1, wherein said interchannel-decorrelated color space comprises an orthogonal color space.

21. The method of claim 1, wherein effects of global lighting and chrominance variations are reduced with said AAM.

22. The method of claim 1, further comprising tracking one or more detected faces through a series of two of more images.

23. An apparatus for detecting faces in a digital image, comprising a processor and one or more processor-readable media for programming the processor to control the apparatus to perform a method comprising:

(a) acquiring a digital color image;

(b) applying an active appearance model (AAM) including an interchannel-decorrelated color space;

(c) matching one or more parameters of the model to the image; and

(d) communicating a face detection result based on the matching or a different result incorporating said face detection result, or both.

24. The apparatus of claim 23, wherein the method further comprises converting RGB data to I₁I₂I₃ color space.

25. The apparatus of claim 24, wherein the converting comprises linear conversion.

26. The apparatus of claim 24, wherein the method further comprises representing texture with the I₁I₂I₃ color space.

27. The apparatus of claim 26, wherein the method further comprises aligning the texture on separate channels.

28. The apparatus of claim 26, wherein the method further comprises performing operations on the texture data on each channel separately.

29. The apparatus of claim 23, wherein said interchannel-decorrleated color space comprises at least three channels including a luminance channel and two chromatic channels.

30. The apparatus of claim 23, wherein the AAM comprises an application of principal components analysis (PCA).

31. The apparatus of claim 30, wherein said PCA comprises eigen-analysis of dispersions of shape, texture and appearance.

32. The apparatus of claim 30, wherein the AAM further comprises an application of generalized procrustes analysis (GPA) including aligning shapes.

33. The apparatus of claim 32, wherein the AAM further comprises a model of shape variability including an application of PCA on a set of shape vectors.

34. The apparatus of claim 33, wherein the AAM further comprises a normalization of objects within the image with respect to shape.

35. The apparatus of claim 34, wherein the AAM further comprises generation of a texture model including sampling intensity information from each shape-free image to form a set of texture vectors.

36. The apparatus of claim 35, wherein the generation of the texture model comprising normalization of the set of texture vectors and application of PCA on the normalized texture vectors.

37. The apparatus of claim 36, wherein the applying comprises retaining only the first one or two of the aligned texture vectors

38. The apparatus of claim 36, wherein the AAM further comprises generation of a combined appearance model including a combined vector from weighted shape parameters concatenated to texture parameters, and application of PCA to the combined vector.

39. The apparatus of claim 23, wherein the matching comprising a regression approach.

40. The apparatus of claim 23, wherein the matching comprises finding model parameters or pose parameters or both.

41. The apparatus of claim 40, wherein the pose parameters comprise translation, scale or rotation, or combinations thereof.

42. The apparatus of claim 23, wherein said interchannel-decorrelated color space comprises an orthogonal color space.

43. The apparatus of claim 23, wherein effects of global lighting and chrominance variations are reduced with said AAM.

44. The apparatus of claim 23, wherein the method further comprises tracking one or more detected faces through a series of two of more images.