Methods involving face model fitting
View Patent ↗A method for face model fitting comprising, receiving a first observed image, receiving a second observed image, and fitting an active appearance model of a third image to the second observed image and the first observed image with an algorithm that includes a first function of a mean-square-error between a warped image of the second observed image and a synthesis of the active appearance model and a second function of a mean-square-error between the warped image of the second observed image and an appearance data of the first observed image.
1. A method for face model fitting comprising:
receiving a first observed image;
receiving a second observed image; and
fitting an active appearance model of a third image to the second observed image and the first observed image with an algorithm that includes a first function of a mean-square-error between a warped image of the second observed image and a synthesis of the active appearance model and a second, function of a mean-square-error between the warped image of the second observed image and an appearance data of the first observed image.
2. The method of claim 1 , further comprising determining whether the warped image of the second observed image and the appearance data of the first observed image correspond to the third image.
3. The method of claim 1 , wherein the appearance data of the first observed image is a warped image of the first observed image.
4. The method of claim 1 , wherein the first observed image and the second observed image are video frames.
5. A method for face model fitting comprising:
receiving a first observed image;
receiving a second observed image;
receiving a third observed image; and
fitting an active appearance model of a fourth image to the third observed image, the second observed image, and the first observed image with an algorithm that includes a first function of a mean-square-error between a warped image of the third observed image and a synthesis of the active appearance model and a second function of a mean-square-error between a warped image of the third observed image and appearance data of the first and second observed images;
wherein the appearance data of the first and second observed images is an average of a warped image of the first observed image and a warped image of the second observed image.
6. The method of claim 5 , further comprising determining whether the warped image of the third observed image and the appearance data of the first and second observed images correspond to the appearance model of the fourth image.
7. The method of claim 5 , wherein the average of the warped image is a decaying average.
8. A method for face model fitting comprising:
receiving a first observed image;
receiving a second observed image;
receiving a third observed image; and
fitting an active appearance model of a fourth image to the third observed image, the second observed image, and the first observed image with an algorithm that includes a first function of a mean-square-error between a warped image of the third observed image and a synthesis of the active appearance model and a second function of a mean-square-error between a warped image of the third observed image and appearance data of the first and second observed images;
wherein the appearance data of the first and second observed images is a dynamic eigenspace model of a warped image of the first observed image and a warped image of the second observed image.
9. The method of claim 5 , wherein the first observed image, the second observed image, and the third observed image are video frames.
10. A method for face model fitting comprising:
receiving a first observed image;
receiving a second observed image; and
fitting an active appearance model of a third image to the second observed image and the first observed image with an algorithm
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where T is a mean appearance, A i is an i th appearance basis, λ=[λ 1 , λ 2 , . . . , λ m ] are appearance parameters, I(W(x,p)) is an image warped from the second observed image, k is a constant value, S 0 is a mean shape, x is a pixel coordinate defined by the mean shape, p=[p1, p2, . . . , pn] are shape parameters, and the M t (x) function includes appearance information from the first observed image.
11. The method of claim 10 , wherein the M t (x) function is defined as M t (x)=I t-1 (W(x; P t-1 )), where t is a time associated with the second observed image and t−1 is a time associated with the first observed image.
12. The method of claim 10 , wherein the method further comprises receiving a plurality of observed images from video frames,
defining a group of L number of images, wherein the group includes the plurality of observed images and the first observed image; and
defining the M t (x) function as
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r is a decaying factor between 0 and 1, and t is a time associated with the second observed image and t−1 is a time associated with the first observed image.
13. The method of claim 10 , wherein the method further comprises
receiving a plurality of observed images from video frames,
defining a group of L number of images, wherein the group includes the plurality of observed images and the first observed image; and
defining the M t (x) function as an average of warped images of the group of L number of images.
14. The method of claim 10 ,
receiving a plurality of observed images from video frames,
defining a group of L number of images, wherein the group includes the plurality of observed images and the first observed image; and
defining the M t (x) function as a dynamic eigenspace model of warped images of the group of L number of images.