Methods and devices of illuminant estimation referencing facial color features for automatic white balance

• US RE47,960 E1
• Filed: 03/23/2017
• Issued: 04/21/2020
• Est. Priority Date: 12/10/2014
• Status: Active Grant

• Current Assignee: Real Imaging Technology Co.Ltd
• Original Assignee: Real Imaging Technology Co.Ltd
• Inventors: Huai, Xiaoning
• Primary Examiner(s): Whittington, Kenneth
• Application Number:
  US15/466,867
• Time in Patent Office: 1,125 Days
• Field of Search: 3482231, 382162, 382167, 382274
• CPC Class Codes: N/A

Abstract

An automatic white balance method and an electronic media device incorporating the method deploy characterized individual human facial color features set of a user or users of the device as reference color surfaces in automatic white balance illuminant estimation and color adjustments, which could improve illuminant estimation accuracy, provide flexible color adjustment options and render the image color to each user'"'"'s own satisfaction taking into account user preferred rendition as target values through a human-machine interactive interface.

Claims

1. A method of automatic white balance illuminant estimation referencing facial color features for a color processing of image and and/or video media in an one or more electronic media device, devices comprising the steps of:

acquiring one or a plurality of digital images of scenes including a face of a user or users of the device, including;

capturing a digital image or images of a user or users using a lens and image sensor, receiving a digital image or images of a user or users after capturing using a lens and image sensor, or a combination of the capturing and receiving steps;

acquiring a facial color features set of the user or users including intrinsic values and target values of the color profiles of a skin tone portion, eye ball white and teeth by a color profile extraction process;

acquiring a digital image;

conducting face detection, segmentation and recognition on the digital image, wherein if a recognized face is the face of a user matching a user entry in the facial color features set;

conducting illuminant estimation based on segmented facial features and the acquired facial color features set resulting in C₂, based on parts of the image other than the segmented facial parts features, resulting in C₁, and determining a final estimation, C₃, based on C₁ and C₂; and

conducting automatic white balance gain adjustment and color enhancement based on C₁, C₂, C₃, and the acquired facial color features set of the user or users

acquiring a facial color features set of one or more users;

acquiring a digital image;

identifying successfully a face in the digital image matching the face of a user, wherein data of facial color features of the user have been acquired in the facial color features set;

acquiring data of facial color profile of at least one facial part of facial skin tone area, eye ball white and teeth from the face of the user in the digital image;

conducting an illuminant estimation of the face of the user in the digital image based on the acquired data of facial color profile of the at least one facial part of the facial skin tone area, eye ball white and teeth from the face of the user in the digital image referencing data of facial color features of the user in the facial color features set.

2. The method of claim 1, further comprising an integrated implementation scheme and distributed implementation schemes, wherein:

the integrated implementation scheme accomplishes the method of claim 1 in a single device having at least a camera in a front-side operational mode, including a mobile phone, a digital camera a camcorder, a laptop computer, a desktop computer, a desk or mobile computing or communication device, and a color imaging device, and

the distributed implementation scheme accomplishes the method of claim 1 by more than one devices with at least one device having at least a camera in an front-side operational mode, wherein the step of capturing a digital image or images is carried out in a first device, acquiring the facial color features set is carried out in a second device, and conducting the illuminant estimation, the automatic white balance gain adjustment, and the color enhancement is carried out in a third device, or another combination of devices.

3. The method of claim 1, wherein the step of acquiring a facial color features set comprises:

capturing an image including a face of a user under a set of standard reference illuminations of known color profiles or capturing an image including a face of a user and using a standard reference gray chart in a variety of common shooting conditions and determining illumination profiles of the shooting conditions by analyzing the gray chart;

conducting face detection, segmentation, and contour location of face skin, the eyeball white and the teeth;

extracting the intrinsic values of the facial color features set of the face skin, the eyeball white and the teeth by averaging pixels in each segment after filtering the pixels using a luminance threshold filter and a histogram-based color filter;

extracting the target values of the facial color features by applying the capturing step, conducting step, and intrinsic value extracting step to images enhanced by user choice, user adjustment, or a combination of user choice and user adjustment, using a user-device interface and transform functions corresponding to the user-choice and/or user-adjustment operations performed on the captured image.

4. The method of claim 1, wherein the step of acquiring a facial color features set comprises a machine learning mode, wherein the machine learning mode is activated or re-activated by a user or a system service routine, and one or more processors:

start illuminant estimation of C₁ in an unsupervised learning mode;

if a certain illuminant estimation, C₁₀, has a confidence level, w₁, higher than a threshold, w_t, conduct face detection, segmentation recognition of a buffered image, and if a face of a user matches a user entry in the facial color features set, locate the contours of the face skin, the eyeball white and the teeth, extract the intrinsic values of the facial color features of the face skin, the eyeball white and the teeth by averaging pixels after filtering using a first luminance threshold filter and a second histogram-based color filter, and store the intrinsic values data in a candidate buffer;

if for the illuminant estimation C₁₀, the differences between the intrinsic values stored in the candidate buffer are smaller than a threshold with a repetitive appearance probability, P₁, which is bigger than a threshold Pit, the illuminant estimation C₁₀ is considered accurate, and an average of the intrinsic values within the difference threshold stored in the candidate buffer is computed and added into a related intrinsic values position of the user entry in the facial color features set;

in a supervised learning mode, display, on a display device, an image after automatic white balance adjustments adjusting the image color under the illumination estimated by C₁₀ to under a standard reference illumination or a user-favored illumination condition followed by color correction, and use a user-machine interactive interface to prompt for and acquire user'"'"'s opinion about a color rendition of the image;

if the user confirms that the color rendition is satisfactory, extract intrinsic values of the user under the estimated illumination, C₁₀, using the same procedure described in the unsupervised learning mode and add the intrinsic values to the related intrinsic values position of the user entry in the facial color features set;

in a mode combining the supervised and unsupervised learning procedures, after assessing the accuracy of the illuminant estimation, C₁₀, in the unsupervised learning mode, conduct a further evaluation using the same procedure described in the supervised learning mode to confirm the accuracy of illuminant estimation, C₁₀, before related intrinsic values data are added to the facial color features set;

acquire the target values data in the supervised learning mode by displaying enhanced images or provide a mechanism for the user to adjust the captured image under the illumination estimated by C₁₀, or a combination thereof, and prompt for and acquire input from the user;

extract the target values of the facial color features based on user input using the same procedure described in the unsupervised learning mode, and add the target values together with transform functions corresponding to operations on captured image based on user input to the related position of the user entry in the facial color features set; and

complete the learning process after the acquired data in the user entry of the facial color features set cover a sufficient range of color profiles as needed for automatic white balance, and illuminant estimation and automatic white balance adjustments could be carried out using the acquired data in the facial color features set.

5. The method of claim 1, wherein the step of illuminant estimation comprises a specific implementation method comprising the steps of:

using a (B/G, R/G) chromaticity coordinate system for illuminants with a black body radiation color profile;

calculating illuminant estimation, C₁;

i_b denoting a pixel in an image background and in an RGB color space, with its R, G, and B components represented by r_ib, g_ib, b_ib respectively, the total number of pixels of the image background portion denoted by T₁, calculating illuminant estimation for the background, {tilde over (C)}₁, given by coordinates;

6. The method in claim 1, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

applying a same gain offset for the entire image and using C₃ as a user-preferred illuminant estimation and a pre-defined standard reference color profile;

assuming C₃ being represented by a coordinate set (R1, G, B1) in RGB space, multiplying the R and B color component of each pixel of the image by a factor G/R1 and G/B1 respectively; and

applying color correction after automatic white balance gain adjustment by a 3*3 color correction matrix with constant coefficients, expressed as [R′

, G′

, B′

]=A*[R,G,B]_T.

7. The method of claim 1, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

adjusting the facial segments and a naked skin tone area under illuminant estimation, C₂, to a user-preferred target position defined as the target values in the user entry of the facial color features set; and

color of the rest of the image illuminant estimation, C₁, to under a standard reference illumination position, by a first procedure comprising the steps of;

looking up for in the facial color features set and applying to the facial area and the naked skin tone area the same transform function as in the process transforming the original captured image to the enhanced image, with adjusted parameters in the function for a rendered image to match the target values corresponding to illuminant estimation, C₂;

adjusting color of the rest of the image under illuminant estimation, C₁, to under a standard reference illumination position;

smoothing boundaries where heterogeneous adjustments occur;

or by a second procedure comprising the steps of;

assuming R_current, B_current and G_current as the estimated skin tone under current illuminant estimation by C₂ and R_ref, B_ref and G_ref to be current target values for face skin corresponding to C₂, calculating

9. The electronic media device of claim 8, wherein the automatic white balance method further comprises an integrated implementation scheme and distributed implementation schemes, wherein:

the integrated implementation scheme accomplishes the method of claim 8 in a single device having at least a camera in a front-side operational mode, including a mobile phones, a digital camera, a camcorder, a laptop computer, and a desktop computer, a desk or mobile computing or communication device, and a color imaging device, and

the distributed implementation scheme accomplishes the method of claim 1 by more than one devices with at least one device having at least a camera in an front-side operational mode, wherein the step of capturing a digital image or images is carried out in a first device, acquiring the facial color features set is carried out in a second device, and conducting the illuminant estimation, the automatic white balance gain adjustment, and the color enhancement is carried out in a third device, or another combination of devices.

18. The one or more non-transitory program storage devices of claim 17, wherein the method further comprises an integrated implementation scheme and a distributed implementation scheme, wherein:

the integrated implementation scheme accomplishes the method of claim 1 in a single device having at least a camera in a front-side operational mode, including a mobile phone, a digital camera, a camcorder, a laptop computer, a desktop computer, a desk or mobile computing or communication device, and a color imaging devices, and

the distributed implementation scheme accomplishes the method of claim 17 by more than one devices with at least one device having at least a camera in an front-side operational mode, wherein the step of capturing a digital image or images is carried out in a first device;

acquiring the facial color features set is carried out in a second device, and conducting the illuminant estimation, the automatic white balance gain adjustment, and the color enhancement is carried out in a third device, or a combination of the first device, second device, and third device.

24. The method of claim 1, wherein the color processing further comprises the steps of:

conducting an illuminant estimation of other parts than the face of the user in the digital image;

conducting an illuminant estimation of the entire digital image based on the illuminant estimation of the face of the user and the illuminant estimation of the other parts than the face of the user in the digital image;

conducting an automatic white balance gain adjustment and/or a color correction and/or a color enhancement of the digital image based on the illuminant estimations.

25. The method of claim 1 or claim 24, comprising an integrated implementation scheme or a distributed implementation scheme, wherein

the integrated implementation scheme accomplishes all the steps in a single electronic media device having at least a camera in a frontside operational mode, and

the distributed implementation scheme accomplishes all the steps in a plurality of electronic media devices.

26. The method of claim 1, wherein the acquiring a facial color features set comprises being initiated and re-initiable by a user and/or a service routine, comprising one or more of the following Procedures:

Procedure 1;

acquiring an image including a face of a user under each of a set of standard reference illuminations of known color profiles;

conducting face detection and segmentation of facial skin tone area eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 2;

acquiring an image including a face of a user and a reference gray chart in a variety of common shooting conditions and determining a color profile of an illumination in each of the shooting conditions by analyzing the reference gray chart;

corresponding to the determined color profile of the illumination in each of the shooting conditions,

conducting a face detection and segmentation of facial skin tone area, eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 3;

acquiring at least one set of data of color profile including coordinates in a color space of facial skin tone area of a user through Procedure 1 or Procedure 2, wherein the face of the user is illuminated by an illumination of a known or determined color profile;

conducting an extrapolation within human facial skin tone statistical distribution trajectory in a color space to obtain more data of the color profile of the facial skin tone area of the user;

Procedure 4;

acquiring the facial color features set of one or more users a machine learning including from acquired images comprising a face or faces of the one or more users.

27. The method of claim 1 or 24, further comprising a user and/or a service routine on the one electronic media device or one of the electronic media devices setting a priority rank number associated with an entry of a user in the facial color features set, wherein the automatic white balance and/or the color correction and/or the color enhancement are biased in an order favoring the users with highest priority rank numbers when multiple faces of users are identified in an acquired digital image.

8. An electronic media device comprises:

one or more cameras with at least one camera working in a front-side operational mode;

one or more processors with one working as a system controller controlling system operation of the electronic media device;

non-transitory memory storage media containing instructions readable by the one or more processors;

memory storage media module accessible by the one or more processors to store data and execute the controlling instructions;

an apparatus with mechanisms to receive, store or output images, videos and other data;

a display device being able to display images, videos and graphics content;

a user controllable interactive interface to the electronic media device that takes in the user control input so that the system controller understands and properly responds to the user intention;

an automatic white balance control apparatus in the camera working in the front-side operational mode incorporating an automatic white balance method comprising the steps of;

acquiring one or a plurality of digital images of scenes including a face of a user or users of the device, including;

capturing a digital image or images of a user or users using a lens and image sensor, receiving a digital image or images of a user or users after capturing using a lens and image sensor, or a combination of the capturing and receiving steps;

acquiring a facial color features set of the user or users including intrinsic values and target values of the color profiles of a skin tone portion, eye ball white and teeth by a color profile extraction process;

acquiring a digital image;

conducting face detection, segmentation and recognition on the digital image, wherein if a recognized face is the face of a user matching a user entry in the facial color features set, conducting illuminant estimation based on segmented facial features and the acquired facial color features set resulting in C₂, based on parts of the image other than the segmented facial parts features, resulting in C₁, and determining a final estimation, C₃, based on C₁ and C₂; and

conducting automatic white balance gain adjustment and color enhancement based on C₁, C₂, C₃, and the acquired facial color features set of the user or users.

10. The electronic media device of claim 8, wherein the steps of acquiring a facial color features set comprises:

capturing an image including a face of a user under a set of standard reference illuminations of known color profiles or capturing an image including a face of a user and using a standard reference gray chart in a variety of common shooting conditions and determining illumination profiles of the shooting conditions by analyzing the gray chart;

conducting face detection, segmentation, and contour location of face skin, the eyeball white and the teeth;

extracting the intrinsic values of the facial color features set of the face skin, the eyeball white and the teeth by averaging pixels in each segment after filtering the pixels using a luminance threshold filter and a histogram-based color filter;

extracting the target values of the facial color features by applying the capturing step, conducting step, and intrinsic value extracting step to images enhanced by user choice, user adjustment, or a combination of user choice and user adjustment, using a user-device interface and transform functions corresponding to the user-choice and/or user-adjustment operations performed on the captured image.

11. The electronic media device of claim 8, wherein the step of acquiring a facial color features set comprises a machine learning mode, wherein the machine learning mode is activated or re-activated by a user or a system service routine, and one or more processors:

start illuminant estimation of C₁ in an unsupervised learning mode;

if a certain illuminant estimation, C₁₀, has a confidence level, w₁, higher than a threshold, w₁, conduct face detection, segmentation recognition of a buffered image, and if a face of a user matches a user entry in the facial color features sets locate the contours of the face skin, the eyeball white and the teeth, extract the intrinsic values of the facial color features of the face skin, the eyeball white and the teeth by averaging pixels after filtering using a first luminance threshold filter and a second histogram-based color filter, and store the intrinsic values data in a candidate buffer;

if for the illuminant estimation C₁₀, the differences between the intrinsic values stored in the candidate buffer are smaller than a threshold with a repetitive appearance probability, P₁, which is bigger than a threshold P_1t, the illuminant estimation C₁₀ is considered accurate, and an average of the intrinsic values within the difference threshold stored in the candidate buffer is computed and added into a related intrinsic values position of the user entry in the facial color features set;

in a supervised learning mode, display, on a display device, an image after automatic white balance adjustments adjusting the image color under the illumination estimated by C₁₀ to under a standard reference illumination or a user-favored illumination condition followed by color correction, and use a user-machine interactive interface to prompt for and acquire user'"'"'s opinion about a color rendition of the image;

if the user confirms that the color rendition is satisfactory, extract intrinsic values of the user under the estimated illumination, C₁₀, using the same procedure described in the unsupervised learning mode and add the intrinsic values to the related intrinsic values position of the user entry in the facial color features set;

in a mode combining the supervised and unsupervised learning procedures, after assessing the accuracy of the illuminant estimation, C₁₀, in the unsupervised learning mode, conduct a further evaluation using the same procedure described in the supervised learning mode to confirm the accuracy of illuminant estimation, C₁₀, before related intrinsic values data are added to the facial color features set;

acquire the target values data in the supervised learning mode by displaying enhanced images or provide a mechanism for the user to adjust the captured image under the illumination estimated by C₁₀, or a combination thereof, and prompt for and acquire input from the user;

extract the target values of the facial color features based on user input using the same procedure described in the unsupervised learning mode, and add the target values together with transform functions corresponding to operations on captured image based on user input to the related position of the user entry in the facial color features set; and

complete the learning process after the acquired data in the user entry of the facial color features set cover a sufficient range of color profiles as needed for automatic white balance, and illuminant estimation and automatic white balance adjustments could be carried out using the acquired data in the facial color features set.

12. The electronic media device of claim 8, wherein the step of illuminant estimation comprises a specific implementation method comprising the steps of:

using a (B/G, R/G) chromaticity coordinate system for illuminants with a black body radiation color profile;

calculating illuminant estimation, C₁;

i_b denoting a pixel in an image background and in an RGB color space, with its R, G, and B components represented by r_ib, g_ib, b_ib respectively, the total number of pixels of the image background portion denoted by T₁, calculating illuminant estimation for the background, {tilde over (C)}₁, given by coordinates,

13. The electronic media device of claim 8, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

applying same gain offset for the entire image and using C₃ as a user-preferred illuminant estimation and a pre-defined standard reference color profile;

assuming C₃ being represented by a coordinate set (R1, G, B1) in RGB space, multiplying the R and B color component of each pixel of the image by a factor G/R1 and G/B1 respectively; and

applying color correction after automatic white balance gain adjustment by a 3*3 color correction matrix with constant coefficients, expressed as [R′

, G′

, B′

]=A*[R,G,B]_T.

14. The electronic media device in claim 8, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

adjusting the facial segments and a naked skin tone area under illuminant estimation, C₂, to a user-preferred target position defined as the target values in the user entry of the facial color features set; and

color of the rest of the image illuminant estimation, C₁, to under a standard reference illumination position, by a first procedure comprising the steps of;

looking up for in the facial color features set and applying to the facial area and the naked skin tone area the same transform function as in the process transforming the original captured image to the enhanced image, with adjusted parameters in the function for a rendered image to match the target values corresponding to illuminant estimation, C₂;

adjusting color of the rest of the image under illuminant estimations, C₁, to under a standard reference illumination position;

smoothing boundaries where heterogeneous adjustments occur;

or by a second procedure comprising the steps of;

assuming R_current, B_current and G_current as the estimated skin tone under current illuminant estimation by C₂ and R_ref, B_ref and G_ref to be current target values for face skin corresponding to C₂, calculating

15. The electronic media device in claim 8, wherein the automatic white balance method comprises a prioritized multiple user operational mode, wherein:

an owner or an administrator or a service routine of the device sets a priority rank number associated with each user entry in the facial color features set, so that the automatic white balance adjustment and color enhancement is biased to render image quality in an order favoring users with higher priority rank numbers when multiple faces of users are recognized in an image.

16. The electronic media device in claim 8, wherein the automatic white balance of the front-side camera comprising an operational mode to work concurrently with and support the automatic white balance illuminant estimation of the back-side camera, wherein:

the back-side camera requires the assistance of the illuminant estimation by the front-side camera through a service request to the system controller, and the system controller endorses the request when no resource or application conflict is found, and if the front-side camera is not in use by the user, launches the front-side camera, and the front-side camera conducts illuminant estimation and outputs the illuminant estimation results to the back-side camera;

the back-side camera uses the illuminant estimation results by the front-side camera as a reference for illuminant estimation by the back-side camera in situations comprising;

the fields of view of both cameras are both indoor or outdoor;

the distances of fields of view of both cameras are close to the device;

the color profile of light source detected by the front-side camera is the same or close to the illuminant estimation by the back-side camera;

the confidence level for illuminant estimation by the back-side camera is too low;

the illuminant estimation of both cameras are the same or close, or a combination thereof.

23. The one or more non-transitory program storage devices of claim 16, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

adjusting the facial segments and a naked skin tone area under illuminant estimation, C₂, to a user-preferred target position defined as the target values in the user entry of the facial color features set; and

color of the rest of the image illuminant estimation, C₁, to under a standard reference illumination position, by a first procedure comprising the steps of;

looking up for in the facial color features set and applying to the facial area and the naked skin tone area the same transform function as in the process transforming the original captured image to the enhanced image, with adjusted parameters in the function for a rendered image to match the target values corresponding to illuminant estimation, C₂;

adjusting color of the rest of the image under illuminant estimation, C₁, to under a standard reference illumination position;

smoothing boundaries where heterogeneous adjustments occur;

or by a second procedure comprising the steps of;

assuming R_current, B_current and G_current as the estimated skin tone under current illuminant estimation by C₂ and R_ref, B_ref and G_ref to be current target values for face skin corresponding to C₂, calculating

17. One or more non-transitory program storage devices, containing instructions readable by one or more processors for causing the one or more processors to perform a method of automatic white balance, comprising the steps of:

acquiring one or a plurality of digital images of scenes including a face of a user or users of the device, including;

capturing a digital image or images of a user or users using a lens and image sensor, receiving a digital image or images of a user or users after capturing using a lens and image sensor, or a combination of the capturing and receiving steps;

acquiring a facial color features set of the user or users including intrinsic values and target values of the color profiles of a skin tone portion, eye ball white and teeth by a color profile extraction process;

acquiring a digital image;

conducting face detection, segmentation and recognition on the digital image, wherein if a recognized face is the face of a user matching a user entry in the facial color features set, conducting illuminant estimation based on segmented facial features and the acquired facial color features set resulting in C₂, based on parts of the image other than the segmented facial parts features, resulting in C₁, and determining a final estimation, C₃, based on C₁ and C₂; and

conducting automatic white balance gain adjustment and color enhancement based on C₁, C₂, C₃, and the acquired facial color features set of the user or users.

19. The one or more non-transitory program storage devices of claim 17, wherein step of acquiring a facial color features set comprises:

capturing an image including a face of a user under a set of standard reference illuminations of known color profiles or capturing an image including a face of a user and using a standard reference gray chart in a variety of common shooting conditions and determining illumination profiles of the shooting conditions by analyzing the gray chart;

conducting face detection, segmentation, and contour location of face skin, the eyeball white and the teeth;

extracting the intrinsic values of the facial color features set of the face skin, the eyeball white and the teeth by averaging pixels in each segment after filtering the pixels using a luminance threshold filter and a histogram-based color filter;

extracting the target values of the facial color features by applying the capturing step, conducting step, and intrinsic value extracting step to images enhanced by user choice, user adjustment, or a combination of user choice and user adjustment, using a user-device interface and transform functions corresponding to the user-choice and/or user-adjustment operations performed on the captured image.

20. The one or more non-transitory program storage devices of claim 17, wherein the step of acquiring a facial color features set comprises:

capturing an image including a face of a user under a set of standard reference illuminations of known color profiles or capturing an image including a face of a user and using a standard reference gray chart in a variety of common shooting conditions and determining illumination profiles of the shooting conditions by analyzing the gray chart;

conducting face detection, segmentation, and contour location of face skin, the eyeball white and the teeth;

extracting the intrinsic values of the facial color features set of the face skin, the eyeball white and the teeth by averaging pixels in each segment after filtering the pixels using a luminance threshold filter and a histogram-based color filter;

extracting the target values of the facial color features by applying the capturing step, conducting step, and intrinsic value extracting step to images enhanced by user choice, user adjustment, or a combination of user choice and user adjustment, using a user-device interface and transform functions corresponding to the user-choice and/or user-adjustment operations performed on the captured image.

21. The one or more non-transitory program storage devices of claim 17, wherein the step of illuminant estimation comprises a specific implementation method comprising the steps of:

using a (B/G, R/G) chromaticity coordinate system for illuminants with a black body radiation color profiles;

calculating illuminant estimation, C₁;

i_b denoting a pixel in an image background and in an RGB color space, with its R, G, and B components represented by r_ib, g_ib, b_ib respectively, the total number of pixels of the image background portion denoted by T₁, calculating illuminant estimation for the background, {tilde over (C)}₁, given by coordinates;

22. The one or more non-transitory program storage devices of claim 17, wherein the step of automatic white balance gain adjustment and color enhancement comprises:

applying a same gain offset for the entire image; and

using C₃ as a user-preferred illuminant estimation and a pre-defined standard reference color profile;

assuming C₃ being represented by a coordinate set (R1, G, B1) in RGB space, multiplying the R and B color component of each pixel of the image by a factor G/R1 and G/B1 respectively; and

applying color correction after automatic white balance gain adjustment by a 3*3 color correction matrix with constant coefficients, expressed as [R′

, G′

, B′

]=A*[R,G,B]_T.

28. An electronic media device comprising one or more cameras including at least a front side operational camera, a device controller, one or more processors and/or programmable and/or configurable hardware computational circuitries, control logic circuitries, user-device interfaces, communication modules for receiving or sending digital data, memory storage modules having instructions and data recorded and/or received and/or processed thereon causing the electronic media device to perform a color processing of acquired images and/or videos by the electronic media device comprising the steps of:

acquiring a facial color features set of one or more users;

acquiring a digital image;

identifying successfully a face in the digital image matching the face of a user, wherein data of facial color features of the user have been acquired in the facial color features set;

acquiring data of facial color profile of at least one facial part of facial skin tone area, eye ball white and teeth from the face of the user in the digital image;

conducting an illuminant estimation of the face of the user in the digital image based on the acquired data of facial color profile of the at least one facial part of the facial skin tone area, eye ball white and teeth from the face of the user in the digital image referencing data of facial color features of the user in the facial color features set;

conducting an illuminant estimation of other parts than the face of the user in the digital image;

conducting an illuminant estimation of the entire digital image based on the illuminant estimation of the face of the user and the illuminant estimation of the other parts than the face of the user in the digital image;

conducting an automatic white balance gain adjustment and/or a color correction and/or a color enhancement of the digital image based on the illuminant estimations.

29. The electronic media device of claim 28, wherein the step of acquiring a facial color features set comprises being initiated and re-initiable by a user and/or a service routine of the electronic media device, comprising one or more of the following Procedures:

Procedure 1;

acquiring an image including a face of a user under each of a set of standard reference illuminations of known color profiles;

conducting face detection and segmentation of facial skin tone area, eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 2;

acquiring an image including a face of a user and a reference gray chart in a variety of common shooting conditions and determining a color profile of an illumination in each of the shooting conditions analyzing the reference gray chart;

corresponding to the determined color profile of the illumination in each of the shooting conditions,

conducting a face detection and segmentation of facial skin tone area, eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 3;

acquiring at least one set of data of color profile including coordinates in a color space of facial skin tone area of a user through Procedure 1 or Procedure 2, wherein the face of the user is illuminated by an illumination of a known or determined color profile;

conducting an extrapolation within human facial skin tone statistical distribution trajectory in a color space to obtain more data of the color profile of the facial skin tone area of the user;

acquiring the facial color features set of one or more users by a machine learning including from acquired images comprising a face or faces of the one or more users.

30. The electronic media device of claim 28, further comprising a user and/or a service routine on the electronic media device setting a priority rank number associated with an entry of a user in the facial color features set, wherein the automatic white balance and/or the color correction and/or the color enhancement are biased in an order favoring the users with highest priority rank numbers when multiple faces of users are identified in an acquired digital image.

31. A method of illuminant estimation referencing color profile of facial skin tone area for a color processing of image and/or video media in one or more electronic media devices comprising the steps of:

acquiring a collection of data of color profile of facial skin tone area of one or more users;

acquiring a digital image;

identifying successfully a face in the digital image matching the face of a user, wherein data of color profile of facial skin tone area of the user have been acquired in the collection of data of color profile of facial skin tone area of at least a user;

acquiring data of color profile of facial skin tone area from the face of the user in the digital image;

conducting illuminant estimation of the face of the user in the digital image based on the acquired data of color profile of the facial skin tone area of the user in the digital image referencing the data of color profile of facial skin tone area of the user in the collection of data of color profile of facial skin tone area of one or more users.

32. The method of claim 31, further comprising the steps of:

conducting an illuminant estimation of other parts than the face of the user in the digital image;

conducting an illuminant estimation of the entire digital image based on the illuminant estimation of the face of the user and the illuminant estimation of the other parts than the face of the user in the digital image;

conducting an automatic white balance pain adjustment and/or a color correction and/or a color enhancement of the digital image based on the illuminant estimations.

33. The method of claim 31 or claim 32, comprises an integrated implementation scheme or a distributed implementation scheme, wherein

the integrated implementation scheme accomplishes all the steps in a single electronic media device having at least a camera in a frontside operational mode, and

the distributed implementation scheme accomplishes all the steps in a plurality of electronic media devices.

34. The method of claim 31, wherein the step of acquiring a collection of data of color profile of facial skin tone area of one or more users comprises being initiated and re-initiable by a user and/or a service routine, comprising one or more of the following Procedures:

Procedure 1;

acquiring an image including a face of a user under each of a set of standard reference illuminations of known color profiles;

conducting face detection and segmentation of facial skin tone area, eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 2;

acquiring an image including a face of a user and a reference gray chart in a variety of common shooting conditions and determining a color profile of an illumination in each of the shooting conditions by analyzing the reference gray chart;

corresponding to the determined color profile of the illumination in each of the shooting conditions,

conducting a face detection and segmentation of facial skin tone area, eyeball white and teeth of the face of the user;

extracting data of color profiles including coordinates in a color space of the facial skin tone area, eyeball white and teeth from the face of the user;

Procedure 3;

acquiring at least one set of data of color profile including coordinates in a color space of facial skin tone area of a user through Procedure 1 or Procedure 2, wherein the face of the user is illuminated by an illumination of a known or determined color profile;

conducting an extrapolation within human facial skin tone statistical distribution trajectory in a color space to obtain more data of the color profile of the facial skin tone area of the user;

Procedure 4;

acquiring the facial color features set of one or more users a machine learning including from acquired images comprising a face or faces of the one or more users.

35. The method of claim 31 or 32, further comprising a user and/or a service routine on the one electronic media device or one of the electronic media devices setting a priority rank number associated with an entry of a user in the facial color features set, wherein the automatic white balance and/or the color correction and/or the color enhancement are biased in an order favoring the users with highest priority rank numbers when multiple faces of users are identified in an acquired digital image.