Method and system using a data-driven model for monocular face tracking

US9400921B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9400921-B2
Application numberUS-85239801-A
CountryUS
Kind codeB2
Filing dateMay 9, 2001
Priority dateMay 9, 2001
Publication dateJul 26, 2016
Grant dateJul 26, 2016

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  1. Title

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  2. Abstract

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  3. Assignees and inventors

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  4. Key dates

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  5. First independent claim

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  6. CPC / IPC classifications

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Abstract

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A method and system using a data-driven model for monocular face tracking are disclosed, which provide a versatile system for tracking three-dimensional (3D) images, e.g., a face, using a single camera. For one method, stereo data based on input image sequences is obtained. A 3D model is built using the obtained stereo data. A monocular image sequence is tracked using the built 3D model. Principal Component Analysis (PCA) can be applied to the stereo data to learn, e.g., possible facial deformations, and to build a data-driven 3D model (“3D face model”). The 3D face model can be used to approximate a generic shape (e.g., facial pose) as a linear combination of shape basis vectors based on the PCA analysis.

First claim

Opening claim text (preview).

What is claimed is: 1. A method for image processing comprising: obtaining stereo data based on input image sequences from of varying facial expressions: building a three-dimensional (3D) model using the obtained stereo data to obtain principal shape vectors; and tracking a second input image sequence using the 3D model to approximate a linear combination of the principal shape vectors of a facial expression in the second input image sequence, wherein the second input image sequence is a monocular image sequence. 2. The method of claim 1 , wherein the building of the 3D model includes processing the obtained stereo data using a Principal Component Analysis (PCA). 3. The method of claim 2 , wherein the processed stereo data using PCA allows the 3D model to approximate a generic shape as the linear combination of the shape basis vectors. 4. The method of claim 1 , wherein the tracking of the monocular image sequence includes tracking of a monocular image sequence of facial deformations using the built 3D model. 5. A computing system comprising: an input unit to stereo data based on input image sequences from of varying facial expressions; and a processing unit to build a three-dimensional (3D) model using the obtained stereo data to approximate a generic shape as a linear combination of shape basis vectors and track a second input image sequence using the 3D model to approximate a linear combination of the principal shape vectors of a facial expression in the second input image sequence, wherein the second input image sequence is a monocular image sequence. 6. The computing system of claim 5 , wherein the processing unit is to process the obtained stereo data using a Principal Component Analysis (PCA). 7. The computing system of claim 6 , wherein the processed stereo data using PCA allows the 3D model to approximate a generic shape as the linear combination of the shape basis vectors. 8. The computing system of claim 5 , wherein the processing unit is to track a monocular image sequence of facial deformations using the built 3D model. 9. A non-transitory machine-readable medium providing instructions, which if executed by a processor, causes the processor to perform an operation comprising: obtaining stereo data based on input image sequences from of varying facial expressions: building a three-dimensional (3D) model using the obtained stereo data to approximate a generic shape as a linear combination of shape basis vectors; and tracking a second input image sequence using the 3D model to approximate a linear combination of the principal shape vectors of a facial expression in the second input image sequence, wherein the second input image sequence is a monocular image sequence. 10. The machine-readable medium of claim 9 , further providing instructions, which if executed by the processor, causes the processor to perform an operation comprising: processing the obtained stereo data using a Principal Component Analysis (PCA). 11. The machine-readable medium of claim 10 , further providing instructions, which if executed by the processor, causes the processor to perform an operation comprising: approximate a generic shape as the linear combination of the shape basis vectors based on the processed stereo data using PCA. 12. The machine-readable medium of claim 9 , further providing instructions, which if executed by the processor, causes the processor to perform an operation comprising: tracking of a monocular image sequence of facial deformations using the built 3D model.

Assignees

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Classifications

  • Video; Image sequence · CPC title

  • involving models · CPC title

  • G06T17/00Primary

    Three-dimensional [3D] modelling for computer graphics · CPC title

  • Depth or disparity estimation from stereoscopic image signals · CPC title

  • Recording image signals; Reproducing recorded image signals · CPC title

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What does patent US9400921B2 cover?
A method and system using a data-driven model for monocular face tracking are disclosed, which provide a versatile system for tracking three-dimensional (3D) images, e.g., a face, using a single camera. For one method, stereo data based on input image sequences is obtained. A 3D model is built using the obtained stereo data. A monocular image sequence is tracked using the built 3D model. Princi…
Who is the assignee on this patent?
Bouguet Jean-Yves, Grzeszczuk Radek, Gokturk Salih, and 1 more
What technology area does this patent fall under?
Primary CPC classification G06T17/00. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Jul 26 2016 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).