Advances in extending the AAM techniques from grayscale to color images
View Patent ↗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.
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 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.