Separating Directional Lighting Variability in Statistical Face Modelling Based on Texture Space Decomposition

• US 20080205712A1
• Filed: 02/27/2008
• Published: 08/28/2008
• Est. Priority Date: 02/28/2007
• Status: Active Grant

• Current Assignee: FotoNation Limited (Adeia Inc.), National University of Ireland Galway
• Original Assignee: FotoNation Vision Limited (Adeia Inc.), National University of Ireland Galway
• Inventors: Bacivarov, Ioana, Corcoran, Peter, Ionita, Mircea
• Granted Patent: US 8,509,561 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06T 5/90 Dynamic range modification of images or parts thereof
• CPC Class Codes: G06V 10/60 relating to illumination properties, e.g. using a reflectance or lighting model
• CPC Class Codes: G06V 10/7557 based on appearance, e.g. active appearance models [AAM]
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation
• CPC Class Codes: G06V 40/171 Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: H04N 23/71 Circuitry for evaluating the brightness variation
• CPC Class Codes: H04N 23/74 by influencing the scene brightness using illuminating means

Abstract

A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Claims

1. A method of determining a characteristic of a face or certain other object within a scene captured in a digital image, comprising:

(a) acquiring a digital image including a face or certain other object within a scene;

(b) applying a linear texture model that is constructed based on a training data set and that comprises a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations;

(c) determining an initial location of the face or certain other object in the scene;

(d) obtaining a fit of said model to said face or certain other object including adjusting one or more individual values of one or more of the model components of said linear texture model;

(e) based on the obtained fit of the model to said face or certain other object in the scene, determining at least one characteristic of the face or certain other object; and

(f) electronically storing, transmitting, applying a face or other object recognition program to, editing, or displaying the corrected face image or certain other object including the determined characteristic, or combinations thereof.

2. The method of claim 1, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.

3. The method of claim 1, wherein the at least one determined characteristic comprises a feature that is independent of directional lighting.

4. The method of claim 1, further comprising generating a reconstructed image without a periodic noise component, including obtaining a second fit of the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components which are without a periodic noise component.

5. The method of claim 4, further comprising:

(i) extracting the periodic noise component including determining a difference between the face or certain other object and the reconstructed image, and

(ii) determining a frequency of the noise component; and

(iii) removing the periodic noise component of the determined frequency.

6. The method of claim 1, further comprising determining an exposure value for the face or certain other object, including obtaining a fit to the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components that exhibit a dependency on exposure value variations.

7. The method of claim 6, further comprising including reducing an effect of a background region or density contrast caused by shadow or both.

8. The method of claim 1, further comprising controlling a flash to accurately reflect a lighting condition, including obtaining a flash control condition by referring to a reference table and controlling a flash light emission according to the flash control condition.

9. The method of claim 8, further comprising reducing an effect of contrasting density caused by shadow or black compression or white compression or combinations thereof

10. The method of claim 1, further comprising adjusting or determining a sharpness value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on sharpness variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a sharpness of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

11. The method of claim 1, further comprising removing a blemish from a face or certain other object, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that do not include such a blemish;

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, removing the blemish from the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

12. The method of claim 11, wherein the blemish comprises an acne blemish or other skin blemish.

13. The method of claim 11, wherein the blemish comprises a photographic artefact.

14. The method of claim 1, further comprising adjusting or determining a graininess value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on graininess variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a graininess of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

15. The method of claim 1, further comprising converting, adjusting or determining a resolution value, or combinations thereof, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit approximately a same resolution as said face or certain other object,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, converting a resolution of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

16. A method of adjusting a characteristic of a face or certain other object within a scene captured in a digital image, comprising:

(a) acquiring a digital image including a face or certain other object within a scene;

(b) applying a linear texture model that is constructed based on a training data set and comprises a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations;

(c) determining an initial location of the face or certain other object in the scene;

(d) obtaining a fit of said model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said linear texture model; and

(e) based on the obtained fit of the model to said face or certain other object in the scene, adjusting at least one characteristic of the face or certain other object including changing one or more values of one or more model components of the linear texture model to generate an adjusted object model;

(f) superimposing the adjusted object model onto said digital image; and

(g) electronically storing, transmitting, applying a face recognition program to, editing, or displaying the corrected face image, or combinations thereof.

17. The method of claim 16, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.

18. The method of claim 16, wherein the at least one determined characteristic comprises a feature that is independent of directional lighting.

19. The method of claim 16, further comprising generating a reconstructed image without a periodic noise component, including obtaining a second fit of the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components which are without a periodic noise component.

20. The method of claim 19, further comprising:

(i) extracting the periodic noise component including determining a difference between the face or certain other object and the reconstructed image, and

(ii) determining a frequency of the noise component; and

(iii) removing the periodic noise component of the determined frequency.

21. The method of claim 16, further comprising determining an exposure value for the face or certain other object, including obtaining a fit to the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components that exhibit a dependency on exposure value variations.

22. The method of claim 21, further comprising including reducing an effect of a background region or density contrast caused by shadow or both.

23. The method of claim 16, further comprising controlling a flash to accurately reflect a lighting condition, including obtaining a flash control condition by referring to a reference table and controlling a flash light emission according to the flash control condition.

24. The method of claim 23, further comprising reducing an effect of contrasting density caused by shadow or black compression or white compression or combinations thereof

25. The method of claim 16, further comprising adjusting or determining a sharpness value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on sharpness variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a sharpness of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

26. The method of claim 16, further comprising removing a blemish from a face or certain other object, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that do not include such a blemish;

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, removing the blemish from the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

27. The method of claim 26, wherein the blemish comprises an acne blemish or other skin blemish.

28. The method of claim 26, wherein the blemish comprises a photographic artefact.

29. The method of claim 16, further comprising adjusting or determining a graininess value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on graininess variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a graininess of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

30. The method of claim 16, further comprising converting, adjusting or determining a resolution value, or combinations thereof, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit approximately a same resolution as said face or certain other object,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, converting a resolution of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

31. The method of claim 16, wherein the adjusting comprises changing one or more values of one or more model components of said first subset of model components to a set of mean values, and thereby adjusting directional lighting effects on the scene within the digital image.

32. The method of claim 31, wherein the adjusting of directional lighting effects comprises increasing one or more directional lighting effects.

33. The method of claim 31, wherein the adjusting of directional lighting effect comprises decreasing one or more directional lighting effects.

34. The method of claim 16, wherein the face or certain other object comprises a face, and the adjusting comprises filtering directional light effects to generate a directional light filtered face image, and the method further comprises applying a face recognition program to the filtered face image.

35. A method for constructing a linear texture model of a class of objects, comprising a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components that are independent of directional lighting variations, comprising:

(a) providing a training set including a plurality of object images wherein various instances of each object cover a range of directional lighting conditions;

(b) applying to the images a linear texture model constructed from object images each captured under uniform lighting conditions and forming a uniform lighting subspace (ULS);

(c) determining a set of residual texture components between object images captured under directional lighting conditions and said linear texture model constructed from object images each captured under uniform lighting conditions;

(d) constructing an orthogonal texture subspace from said residual texture components to form a directional lighting subspace (DLS); and

(e) combining said uniform lighting subspace (ULS) with said directional lighting subspace (DLS) to form a new linear texture model.

36. The method of claim 35, further comprising:

(i) applying the new linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that has uniform lighting subspace (ULS) and directional lighting subspace (DLS) components,

(ii) obtaining a fit of said new model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said new linear texture model;

(iii) based on the obtained fit of the new model to said face or certain other object in the scene, determining a characteristic of the face or certain other object, and

(iv) electronically storing, transmitting, applying a face or other object recognition program to, editing, or displaying the corrected face image or certain other object including the determined characteristic, or combinations thereof.

37. The method of claim 36, further comprising:

(v) further comprising changing one or more values of one or more model components of the new linear texture model to generate a further adjusted object model;

(vi) obtaining a fit of said new model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said new linear texture model; and

(vii) based on the obtained fit of the new model to said face or certain other object in the scene, determining a characteristic of the face or certain other object.

38. The method of claim 35, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.

39. A face illumination normalization method, comprising:

(a) acquiring a digital image including data corresponding to a face that appears to be illuminated unevenly;

(b) applying separate sets of directional and uniform illumination classifier programs to the face data;

(c) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the directional illumination classifier programs plus a constant vector representing said face according to one or a combination of the uniform illumination classifier programs, thereby decomposing the face data into orthogonal subspaces for directional and uniform illumination;

(d) normalizing an illumination condition for the face including setting one or more illumination parameters of the directional illumination projection to zero;

(e) electronically storing, transmitting, applying a face recognition program to, editing, or displaying the corrected face image, or combinations thereof.

40. The method of claim 39, wherein the applying comprises projecting the face data onto the uniform lighting classifier program set, and then applying residual data of the face data to the directional lighting classifier program set.

41. The method of claim 39, further comprising applying a face recognition program to the normalized face image.

42. The method of claim 39, further comprising applying a set of feature detector programs to reject non-face data from being identified as face data.

43. The method of claim 39, further comprising determining an illumination condition based on acceptance of the face data by one or a combination of the directional illumination classifier programs.

44. The method of claim 39, wherein the digital image is one of multiple images in a series that include said face, and wherein said normalizing is applied to a different image in the series than said digital image within which the face data is identified.

45. A face detection method, comprising:

(a) acquiring a digital image

(b) extracting a sub-window from said image

(c) applying separate sets of two or more shortened face detection classifier cascades, trained to be selectively sensitive to a characteristic of a face region, and another set of face detection classifier cascades insensitive to said characteristic;

(d) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the characteristic-sensitive classifier cascades plus a constant vector representing said face according to one or a combination of the characteristic-insensitive classifier cascades, thereby decomposing the face data into orthogonal subspaces for characteristic-sensitive and characteristic-insensitive conditions;

(e) based on the applying and identifying, determining a probability that a face with a certain form of the characteristic is present within the sub-window;

(f) based on the determining, applying an extended face detection classifier cascade trained for sensitivity to said form of said characteristic;

(g) providing a final determination that a face exists within the image sub-window; and

(h) repeating steps (b)-(f) one or more times for one or more further sub-windows from the image or one or more further characteristics, or both.

46. The method of claim 45, wherein the characteristic or characteristics comprise a directional illumination of the face region, an in-plane rotation of the face region, a 3D pose variation of the face region, a degree of smile, a degree of eye-blinking, a degree of eye-winking, a degree of mouth opening, facial blurring, eye-defect, facial shadowing, facial occlusion, facial color, or facial shape, or combinations thereof.

47. The method of claim 45, wherein the characteristic comprises a directional illumination, and the method comprises determining an uneven illumination condition by applying one or more uneven illumination classifier cascades.

48. The method of claim 47, further comprising applying a front illumination classifier cascade.

49. The method of claim 48, further comprising determining an illumination condition of a face within a sub-window based on acceptance by one of the classifier cascades.

50. The method of claim 49, wherein the digital image is one of multiple images in a series that include the face, and the method further comprises correcting an uneven illumination condition of the face within a different image in the series than said digital image within which the illuminating condition is determined.

51. The method of claim 47, wherein said uneven illumination classifier cascades comprise a top illumination classifier, a bottom illumination classifier, and one or both of right and left illumination classifiers.

52. A face detection method, comprising:

(a) acquiring a digital image

(b) extracting a sub-window from said image

(c) applying separate sets of two or more shortened face detection classifier cascades, trained to be selectively sensitive to a directional facial illumination, and another set of face detection classifier cascades insensitive to directional facial illumination;

(d) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the directional illumination classifier cascades plus a constant vector representing said face according to one or a combination of the directional illumination insensitive classifier cascades, thereby decomposing the face data into orthogonal subspaces for directional and uniform conditions;

(e) based on the applying and identifying, determining a probability that a face having a certain form of directional facial illumination is present within the sub-window;

(f) based on the determining, applying an extended face detection classifier cascade trained for sensitivity to said form of directional facial illumination;

(g) providing a final determination that a face exists within the image sub-window

(h) repeating steps (b)-(f) one or more times for one or more further sub-windows from the image or one or more further directional facial illuminations, or both.

53. The method of claim 52, wherein the digital image is one of multiple images in a series that include the face, and the method further comprises correcting an uneven illumination condition of the face within a different image in the series than said digital image within which the illuminating condition is determined.

54. The method of claim 52, wherein said directional illumination classifier cascades comprise a top illumination classifier, a bottom illumination classifier, and one or both of right and left illumination classifiers.

55. The method of claim 52, further comprising determining an illumination condition of a face within a sub-window based on acceptance by one or a combination of the classifier cascades.

56. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a face illumination normalization method, wherein the method comprises:

(a) acquiring a digital image including data corresponding to a face that appears to be illuminated unevenly;

(b) applying separate sets of directional and uniform illumination classifier programs to the face data;

(c) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the directional illumination classifier programs plus a constant vector representing said face according to one or a combination of the uniform illumination classifier programs, thereby decomposing the face data into orthogonal subspaces for directional and uniform illumination;

(d) normalizing an illumination condition for the face including setting one or more illumination parameters of the directional illumination projection to zero;

(e) electronically storing, transmitting, applying a face recognition program to, editing, or displaying the corrected face image, or combinations thereof.

57. The device of claim 56, wherein the applying comprises projecting the face data onto the uniform lighting classifier program set, and then applying residual data of the face data to the directional lighting classifier program set.

58. The device of claim 56, wherein the method further comprises applying a face recognition program to the normalized face image.

59. The device of claim 56, wherein the method further comprises applying a set of feature detector programs to reject non-face data from being identified as face data.

60. The device of claim 56, wherein the method further comprises determining an illumination condition based on acceptance of the face data by one or a combination of the directional illumination classifier programs.

61. The device of claim 56, wherein the digital image is one of multiple images in a series that include said face, and wherein said normalizing is applied to a different image in the series than said digital image within which the face data is identified.

62. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a face illumination normalization method, wherein the method comprises:

(a) acquiring a digital image

(b) extracting a sub-window from said image

(c) applying separate sets of two or more shortened face detection classifier cascades, trained to be selectively sensitive to a characteristic of a face region, and another set of face detection classifier cascades insensitive to said characteristic;

(d) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the characteristic-sensitive classifier cascades plus a constant vector representing said face according to one or a combination of the characteristic-insensitive classifier cascades, thereby decomposing the face data into orthogonal subspaces for characteristic-sensitive and characteristic-insensitive conditions;

(e) based on the applying and identifying, determining a probability that a face with a certain form of the characteristic is present within the sub-window;

(f) based on the determining, applying an extended face detection classifier cascade trained for sensitivity to said form of said characteristic;

(g) providing a final determination that a face exists within the image sub-window; and

(h) repeating steps (b)-(f) one or more times for one or more further sub-windows from the image or one or more further characteristics, or both.

63. The device of claim 62, wherein the characteristic or characteristics comprise a directional illumination of the face region, an in-plane rotation of the face region, a 3D pose variation of the face region. a degree of smile, a degree of eye-blinking, a degree of eye-winking, a degree of mouth opening, facial blurring, eye-defect, facial shadowing, facial occlusion, facial color, or facial shape, or combinations thereof.

64. The device of claim 62, wherein the characteristic comprises a directional illumination, and the method further comprises determining an uneven illumination condition by applying one or more uneven illumination classifier cascades.

65. The device of claim 64, wherein the method further comprises applying a front illumination classifier cascade.

66. The device of claim 65, wherein the method further comprises determining an illumination condition of a face within a sub-window based on acceptance by one of the classifier cascades.

67. The device of claim 66, wherein the digital image is one of multiple images in a series that include the face, and the method further comprises correcting an uneven illumination condition of the face within a different image in the series than said digital image within which the illuminating condition is determined.

68. The device of claim 62, wherein said uneven illumination classifier cascades comprise a top illumination classifier, a bottom illumination classifier, and one or both of right and left illumination classifiers.

69. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a face illumination normalization method, wherein the method comprises:

(a) acquiring a digital image

(b) extracting a sub-window from said image

(c) applying separate sets of two or more shortened face detection classifier cascades, trained to be selectively sensitive to a directional facial illumination, and another set of face detection classifier cascades insensitive to directional facial illumination;

(d) identifying the face data as corresponding to a projection of said face within the digital image on one or a combination of the directional illumination classifier cascades plus a constant vector representing said face according to one or a combination of the directional illumination insensitive classifier cascades, thereby decomposing the face data into orthogonal subspaces for directional and uniform conditions;

(e) based on the applying and identifying, determining a probability that a face having a certain form of directional facial illumination is present within the sub-window;

(f) based on the determining, applying an extended face detection classifier cascade trained for sensitivity to said form of directional facial illumination;

(g) providing a final determination that a face exists within the image sub-window

(h) repeating steps (b)-(f) one or more times for one or more further sub-windows from the image or one or more further directional facial illuminations, or both.

70. The device of claim 69, wherein the digital image is one of multiple images in a series that include the face, and the method further comprises correcting an uneven illumination condition of the face within a different image in the series than said digital image within which the illuminating condition is determined.

71. The device of claim 69, wherein said uneven illumination classifier cascades comprise a top illumination classifier, a bottom illumination classifier, and one or both of right and left illumination classifiers.

72. The device of claim 71, wherein the method further comprises determining an illumination condition of a face within a sub-window based on acceptance by one of the classifier cascades.

73. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a method of determining a characteristic of a face or certain other object within a scene captured in a digital image, wherein the method comprises:

(a) acquiring a digital image including a face or certain other object within a scene;

(b) applying a linear texture model that is constructed based on a training data set and that comprises a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations;

(c) determining an initial location of the face or certain other object in the scene;

(d) obtaining a fit of said model to said face or certain other object including adjusting one or more individual values of one or more of the model components of said linear texture model;

(e) based on the obtained fit of the model to said face or certain other object in the scene, determining at least one characteristic of the face or certain other object; and

(f) electronically storing, transmitting, applying a face or other object recognition program to, editing, or displaying the corrected face image or certain other object including the determined characteristic, or combinations thereof.

74. The device of claim 73, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.

75. The device of claim 73, wherein the at least one determined characteristic comprises a feature that is independent of directional lighting.

76. The device of claim 73, wherein the method further comprises generating a reconstructed image without a periodic noise component, including obtaining a second fit of the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components which are without a periodic noise component.

77. The device of claim 76, wherein the method further comprises:

(i) extracting the periodic noise component including determining a difference between the face or certain other object and the reconstructed image, and

(ii) determining a frequency of the noise component; and

(iii) removing the periodic noise component of the determined frequency.

78. The device of claim 73, wherein the method further comprises determining an exposure value for the face or certain other object, including obtaining a fit to the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components that exhibit a dependency on exposure value variations.

79. The device of claim 78, wherein the method further comprises including reducing an effect of a background region or density contrast caused by shadow or both.

80. The device of claim 73, wherein the method further comprises controlling a flash to accurately reflect a lighting condition, including obtaining a flash control condition by referring to a reference table and controlling a flash light emission according to the flash control condition.

81. The device of claim 80, wherein the method further comprises reducing an effect of contrasting density caused by shadow or black compression or white compression or combinations thereof

82. The device of claim 73, wherein the method further comprises adjusting or determining a sharpness value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on sharpness variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a sharpness of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

83. The device of claim 73, wherein the method further comprises removing a blemish from a face or certain other object, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that do not include such a blemish;

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, removing the blemish from the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

84. The device of claim 83, wherein the blemish comprises an acne blemish or other skin blemish.

85. The device of claim 83, wherein the blemish comprises a photographic artefact.

86. The device of claim 73, wherein the method further comprises adjusting or determining a graininess value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on graininess variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a graininess of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

87. The device of claim 73, wherein the method further comprises converting, adjusting or determining a resolution value, or combinations thereof, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit approximately a same resolution as said face or certain other object,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, converting a resolution of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

88. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a method of adjusting a characteristic of a face or certain other object within a scene captured in a digital image, wherein the method comprises:

(a) acquiring a digital image including a face or certain other object within a scene;

(b) applying a linear texture model that is constructed based on a training data set and comprises a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations;

(c) determining an initial location of the face or certain other object in the scene;

(d) obtaining a fit of said model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said linear texture model; and

(e) based on the obtained fit of the model to said face or certain other object in the scene, adjusting at least one characteristic of the face or certain other object including changing one or more values of one or more model components of the linear texture model to generate an adjusted object model;

(f) superimposing the adjusted object model onto said digital image; and

(g) electronically storing, transmitting, applying a face recognition program to, editing, or displaying the corrected face image, or combinations thereof.

89. The device of claim 88, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.

90. The device of claim 88, wherein the at least one determined characteristic comprises a feature that is independent of directional lighting.

91. The device of claim 88, wherein the method further comprises generating a reconstructed image without a periodic noise component, including obtaining a second fit of the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components which are without a periodic noise component.

92. The device of claim 91, wherein the method further comprises:

(i) extracting the periodic noise component including determining a difference between the face or certain other object and the reconstructed image, and

(ii) determining a frequency of the noise component; and

(iii) removing the periodic noise component of the determined frequency.

93. The device of claim 88, wherein the method further comprises determining an exposure value for the face or certain other object, including obtaining a fit to the face or certain other object to a second linear texture model that is based on a training data set and that comprises a class of objects including a set of model components that exhibit a dependency on exposure value variations.

94. The device of claim 93, wherein the method further comprises including reducing an effect of a background region or density contrast caused by shadow or both.

95. The device of claim 88, wherein the method further comprises controlling a flash to accurately reflect a lighting condition, including obtaining a flash control condition by referring to a reference table and controlling a flash light emission according to the flash control condition.

96. The device of claim 95, wherein the method further comprises reducing an effect of contrasting density caused by shadow or black compression or white compression or combinations thereof

97. The device of claim 88, wherein the method further comprises adjusting or determining a sharpness value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on sharpness variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a sharpness of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

98. The device of claim 88, wherein the method further comprises removing a blemish from a face or certain other object, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that do not include such a blemish;

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, removing the blemish from the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

99. The device of claim 98, wherein the blemish comprises an acne blemish or other skin blemish.

100. The device of claim 98, wherein the blemish comprises a photographic artefact.

101. The device of claim 88, wherein the method further comprises adjusting or determining a graininess value, or both, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit a dependency on graininess variations,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, adjusting a graininess of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

102. The device of claim 88, wherein the method further comprises converting, adjusting or determining a resolution value, or combinations thereof, including:

(i) applying a second linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that exhibit approximately a same resolution as said face or certain other object,

(ii) obtaining a fit of said second model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said second linear texture model; and

(e) based on the obtained fit of the second model to said face or certain other object in the scene, converting a resolution of the face or certain other object including changing one or more values of one or more model components of the second linear texture model to generate a further adjusted object model.

103. The device of claim 88, wherein the adjusting comprises changing one or more values of one or more model components of said first subset of model components to a set of mean values, and thereby adjusting directional lighting effects on the scene within the digital image.

104. The device of claim 103, wherein the adjusting of directional lighting effects comprises increasing one or more directional lighting effects.

105. The device of claim 103, wherein the adjusting of directional lighting effect comprises decreasing one or more directional lighting effects.

106. The device of claim 88, wherein the face or certain other object comprises a face, and the adjusting comprises filtering directional light effects to generate a directional light filtered face image, and the method further comprises applying a face recognition program to the filtered face image.

107. A digital image acquisition device including an optoelectonic system for acquiring a digital image, and a digital memory having stored therein processor-readable code for programming the processor to perform a method for constructing a linear texture model of a class of objects, comprising a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components that are independent of directional lighting variations, wherein the method comprises:

(a) providing a training set including a plurality of object images wherein various instances of each object cover a range of directional lighting conditions;

(b) applying to the images a linear texture model constructed from object images each captured under uniform lighting conditions and forming a uniform lighting subspace (ULS);

(c) determining a set of residual texture components between object images captured under directional lighting conditions and said linear texture model constructed from object images each captured under uniform lighting conditions;

(d) constructing an orthogonal texture subspace from said residual texture components to form a directional lighting subspace (DLS); and

(e) combining said uniform lighting subspace (ULS) with said directional lighting subspace (DLS) to form a new linear texture model.

108. The device of claim 107, wherein the method further comprises:

(i) applying the new linear texture model that is constructed based on a training data set and comprises a class of objects including a subset of model components that has uniform lighting subspace (ULS) and directional lighting subspace (DLS) components,

(ii) obtaining a fit of said new model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said new linear texture model;

(iii) based on the obtained fit of the new model to said face or certain other object in the scene, determining a characteristic of the face or certain other object, and

(iv) electronically storing, transmitting, applying a face or other object recognition program to, editing, or displaying the corrected face image or certain other object including the determined characteristic, or combinations thereof.

109. The device of claim 108, wherein the method further comprises:

(v) further comprising changing one or more values of one or more model components of the new linear texture model to generate a further adjusted object model;

(vi) obtaining a fit of said new model to said face or certain other object in the scene including adjusting one or more individual values of one or more model components of said new linear texture model; and

(vii) based on the obtained fit of the new model to said face or certain other object in the scene, determining a characteristic of the face or certain other object.

110. The device of claim 107, wherein the model components comprise eigenvectors, and the individual values comprises eigenvalues of the eigenvectors.