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WO2002009038A2 - Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image - Google Patents

Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image Download PDF

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Publication number
WO2002009038A2
WO2002009038A2 PCT/US2001/023337 US0123337W WO0209038A2 WO 2002009038 A2 WO2002009038 A2 WO 2002009038A2 US 0123337 W US0123337 W US 0123337W WO 0209038 A2 WO0209038 A2 WO 0209038A2
Authority
WO
WIPO (PCT)
Prior art keywords
face image
facial feature
neutral face
actor
customizing
Prior art date
Application number
PCT/US2001/023337
Other languages
French (fr)
Other versions
WO2002009038A3 (en
Inventor
Ulrich F. Buddenmeier
Hartmut Neven
Original Assignee
Eyematic Interfaces, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eyematic Interfaces, Inc. filed Critical Eyematic Interfaces, Inc.
Priority to KR1020037001107A priority Critical patent/KR100827939B1/en
Priority to AU7714801A priority patent/AU7714801A/en
Priority to AU2001277148A priority patent/AU2001277148B2/en
Priority to JP2002514665A priority patent/JP2004505353A/en
Priority to EP01954934A priority patent/EP1303842A2/en
Publication of WO2002009038A2 publication Critical patent/WO2002009038A2/en
Publication of WO2002009038A3 publication Critical patent/WO2002009038A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00—Animation
    • G06T13/20—3D [Three Dimensional] animation
    • G06T13/40—3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/97—Determining parameters from multiple pictures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
    • G06V10/422—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
    • G06V10/426—Graphical representations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face

Definitions

  • the present invention relates to avatar animation, and more particularly, to facial feature tracking.
  • Virtual spaces filled with avatars are an attractive the way to allow for the experience of a shared environment.
  • animation of a photo-realistic avatar generally requires robust tracking of an actor's movements, particularly for tracking facial features.
  • the present invention satisfies this need.
  • the present invention is embodied in a method, and related system, for customizing a visual sensor using a neutral face image of an actor.
  • the method includes capturing a front neutral face image of an actor and automatically finding facial feature locations on the front neutral face image using elastic bunch graph matching. Nodes are automatically positioned at the facial feature locations on the front neutral face image of the actor. The node positions are then manually corrected on front neutral face image of the actor.
  • the method may include generating a corrector graph based on the corrected node positions.
  • FIG. 1 is a flow diagram for illustrating a method for customizing facial feature tracking using precise landmark finding on a neutral face image, according to the present invention.
  • FIG. 2 is an image of a visual sensor customization wizard having a camera image of an actor and a generic model image.
  • FIG. 3 is an image of a visual sensor customization wizard after automatic sensing and placement of node locations on a camera image of an actor's face.
  • FIG. 4 is an image of a visual sensor customization wizard having corrected node positions for generating a corrector graph, according to the present invention.
  • FIG. 5 is a block diagram of a technique for generating a corrector graph using a neutral face image, according to the present invention.
  • the present invention is embodied in a method and system for customizing a visual sensor for facial feature tracking using a neutral face image of an actor.
  • the method may include generating a corrector graph to improve the sensor's performance in tracking an actor's facial features.
  • the method captures a front face image of the actor (block 12) .
  • the front neutral face image may be captured with the assistance of a visual sensor customization wizard 22, shown in FIG. 2.
  • An example image 24 is shown to the actor to indicate the alignment of the captured image 26.
  • facial feature locations are automatically found using elastic bunch graph matching (block 14). Facial feature finding using elastic bunch graph matching is described in U.S. patent application number 09/188,079.
  • an image is transformed into Gabor space using a wavelet transformations based on Gabor wavelets.
  • the transformed image is represented by complex wavelet component values associated with each pixel of the original image.
  • nodes 28 are automatically placed on the front face image at the locations of particular facial features (block 16).
  • a facial feature graph placed over the actor's front face image may have nodes locations that are not properly placed on the front face image. For example, the four nodes for the actor's eyebrows are placed slightly above the eyebrows on the front face image.
  • the system operator may use the visual sensor customization wizard 22 to pick and move the nodes 28.
  • the nodes are manually moved on the neutral face image 26 using a pointing device, such as a mouse, to select and drag a node to a desired location (block 18). For example, as shown in FIG. 4, node placement on the eyebrows of the actor's image has been adjusted to more closely aligned with the actor's eyebrows in accordance with the example image 24.
  • image jets are recalculated for each facial feature and may be compared to corresponding jets in a gallery 32 of a bunch graph.
  • the bunch graph gallery includes sub-galleries of a large number N of persons.
  • Each person in the sub-gallery includes jets for a neutral face image 34 and for expressive facial images, 36 through 38, such as a smiling face or a face showing exclamation.
  • Each feature jet from the corrected actor image 24 is compared with the corresponding feature jet from the neutral jets in the several sub-galleries.
  • the sub-gallery neutral jet for a feature (i.e., feature A) that most closely matches the jet for the image feature A is selected for generating a jet gallery for the feature A of a corrector graph 40.
  • the sub-gallery for person N has a neutral jet for feature E that most closely corresponds to thejet for feature E from the neutral image 24.
  • the corrector graph jets for facial feature E are generated using thejet for feature E from the neutral jets along with the jets for feature E from each of the expressive feature jets, 36 through 38, from the sub-gallery N. Accordingly, the corrector graph 40 is formed using the best jets, with respect to the neutral face image 24, from the gallery 32 forming the bunch graph.
  • the resulting corrector graph 40 provides a much more robust sensor for tracking node locations.
  • a custom facial feature tracking sensor incorporating the corrector graph may provide a more photo-realistic avatar and an enhanced virtual space experience.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Processing Or Creating Images (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

The present invention is embodied in a method and system for customizing a visual sensor for facial feature tracking using a neutral face image of an actor. The method may include generating a corrector graph to improve the sensor's performance in tracking an actor's facial features.

Description

METHOD AND SYSTEM FOR CUSTOMIZING FACIAL FEATURE
TRACKING USING PRECISE LANDMARK FINDING ON A
NEUTRAL FACE IMAGE
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims priority under 35 U.S.C. §119(e)(l) and 37 C.F.R. § 1.78(a)(4) to U.S. provisional application serial number 60/220,288, entitled METHOD AND SYSTEM FOR CUSTOMIZING FACIAL FEATURE TRACKING USING PRECISE LANDMARK FINDING ON A NEUTRAL FACE IMAGE and filed My 24, 2000; and claims priority under 35 U.S.C. § 120 and 37 C.F.R. § 1.78(a)(2) as a continuation-in-part to U.S. patent application serial number 09/188,079, entitled WAVELET-BASED FACIAL MOTION CAPTURE FOR AVATAR ANIMATION and filed November 6, 1998. The entire disclosure of U.S. patent application serial number 09/188,079 is incorporated herein by reference.
BACKGROUND OF THE INVENTION The present invention relates to avatar animation, and more particularly, to facial feature tracking.
Virtual spaces filled with avatars are an attractive the way to allow for the experience of a shared environment. However, animation of a photo-realistic avatar generally requires robust tracking of an actor's movements, particularly for tracking facial features.
Accordingly, there exists a significant need for improved facial feature tracking. The present invention satisfies this need. SUMMARY OF THE INVENTION The present invention is embodied in a method, and related system, for customizing a visual sensor using a neutral face image of an actor. The method includes capturing a front neutral face image of an actor and automatically finding facial feature locations on the front neutral face image using elastic bunch graph matching. Nodes are automatically positioned at the facial feature locations on the front neutral face image of the actor. The node positions are then manually corrected on front neutral face image of the actor.
Further, the method may include generating a corrector graph based on the corrected node positions.
Other features and advantages of the present invention should be apparent from the following description of the preferred embodiments taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow diagram for illustrating a method for customizing facial feature tracking using precise landmark finding on a neutral face image, according to the present invention.
FIG. 2 is an image of a visual sensor customization wizard having a camera image of an actor and a generic model image.
FIG. 3 is an image of a visual sensor customization wizard after automatic sensing and placement of node locations on a camera image of an actor's face.
FIG. 4 is an image of a visual sensor customization wizard having corrected node positions for generating a corrector graph, according to the present invention. FIG. 5 is a block diagram of a technique for generating a corrector graph using a neutral face image, according to the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is embodied in a method and system for customizing a visual sensor for facial feature tracking using a neutral face image of an actor. The method may include generating a corrector graph to improve the sensor's performance in tracking an actor's facial features.
As shown in FIG. 1, the method captures a front face image of the actor (block 12) . The front neutral face image may be captured with the assistance of a visual sensor customization wizard 22, shown in FIG. 2. An example image 24 is shown to the actor to indicate the alignment of the captured image 26. Next, facial feature locations are automatically found using elastic bunch graph matching (block 14). Facial feature finding using elastic bunch graph matching is described in U.S. patent application number 09/188,079. In the elastic graph matching technique, an image is transformed into Gabor space using a wavelet transformations based on Gabor wavelets. The transformed image is represented by complex wavelet component values associated with each pixel of the original image. As shown in FIG. 3, nodes 28 are automatically placed on the front face image at the locations of particular facial features (block 16). Because of particular image characteristics of the actor, a facial feature graph placed over the actor's front face image may have nodes locations that are not properly placed on the front face image. For example, the four nodes for the actor's eyebrows are placed slightly above the eyebrows on the front face image.
The system operator may use the visual sensor customization wizard 22 to pick and move the nodes 28. The nodes are manually moved on the neutral face image 26 using a pointing device, such as a mouse, to select and drag a node to a desired location (block 18). For example, as shown in FIG. 4, node placement on the eyebrows of the actor's image has been adjusted to more closely aligned with the actor's eyebrows in accordance with the example image 24.
As shown in FIG. 5, after the nodes 28 for features, A through E, are correctly placed on the front neutral face image 24, image jets are recalculated for each facial feature and may be compared to corresponding jets in a gallery 32 of a bunch graph. The bunch graph gallery includes sub-galleries of a large number N of persons. Each person in the sub-gallery includes jets for a neutral face image 34 and for expressive facial images, 36 through 38, such as a smiling face or a face showing exclamation. Each feature jet from the corrected actor image 24 is compared with the corresponding feature jet from the neutral jets in the several sub-galleries. The sub-gallery neutral jet for a feature (i.e., feature A) that most closely matches the jet for the image feature A is selected for generating a jet gallery for the feature A of a corrector graph 40. As a more particular example, for the feature E, the sub-gallery for person N has a neutral jet for feature E that most closely corresponds to thejet for feature E from the neutral image 24. The corrector graph jets for facial feature E are generated using thejet for feature E from the neutral jets along with the jets for feature E from each of the expressive feature jets, 36 through 38, from the sub-gallery N. Accordingly, the corrector graph 40 is formed using the best jets, with respect to the neutral face image 24, from the gallery 32 forming the bunch graph.
The resulting corrector graph 40 provides a much more robust sensor for tracking node locations. A custom facial feature tracking sensor incorporating the corrector graph may provide a more photo-realistic avatar and an enhanced virtual space experience.
Although the foregoing discloses the preferred embodiments of the present invention, it is understood that those skilled in the art may make various changes to the preferred embodiments without departing from the scope of the invention. The invention is defined only by the following claims.

Claims

WE CLAIM:CLAIMS
1. A method for customizing facial feature tracking, comprising: capturing a front neutral face image of an actor; automatically finding facial feature locations on the front neutral face image using elastic bunch graph matching; automatically positioning nodes at the facial feature locations on the front neutral face image of the actor; and manually correcting the positioning of the nodes on front neutral face image of the actor.
2. A method for customizing facial feature tracking as defined in claim 1, further comprising generating a corrector graph based on the corrected node positions.
3. A system for customizing facial feature tracking, comprising: means for capturing a front neutral face image of an actor; means for automatically finding facial feature locations on the front neutral face image using elastic bunch graph matching; means for automatically positioning nodes at the facial feature locations on the front neutral face image of the actor; and means for manually correcting the positioning of the nodes on front neutral face image of the actor.
10
4. A. system for customizing facial feature tracking as defined in claim 3, further comprising means for generating a corrector graph based on the corrected node positions.
5. A method for customizing facial feature tracking, comprismg: 15 capturing a front neutral face image of an actor; automatically finding facial feature locations on the front neutral face image using image analysis based on wavelet component values generated from wavelet transformations of the front neutral face image; automatically positioning nodes at the facial feature locations on the front '20 neutral face image of the actor; and manually correcting the positioning of the nodes on front neutral face image of the actor.
6. A method for customizing facial feature tracking as defined in 25 claim 5, wherein the wavelet transformations use Gabor wavelets.
7. A method for customizing facial feature tracking, comprising: capturing a front neutral face image of an actor; finding facial feature locations on the front neutral face image using image analysis based on wavelet component values generated from wavelet transformations of the front neutral face image; and generating a corrector graph for expressive facial features based on the wave component values at the facial feature locations on the front neutral face image.
8. A method for customizing facial feature tracking as defined in claim 7, wherein the wavelet transformations use Gabor wavelets.
PCT/US2001/023337 2000-07-24 2001-07-24 Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image WO2002009038A2 (en)

Priority Applications (5)

Application Number Priority Date Filing Date Title
KR1020037001107A KR100827939B1 (en) 2000-07-24 2001-07-24 Methods and systems for customizing facial feature tracking using precision landmark discovery on expressionless face images
AU7714801A AU7714801A (en) 2000-07-24 2001-07-24 Method and system for customizing facial feature tracking using precise landmarkfinding on a neutral face image
AU2001277148A AU2001277148B2 (en) 2000-07-24 2001-07-24 Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image
JP2002514665A JP2004505353A (en) 2000-07-24 2001-07-24 Facial feature special method and device by accurate landmark detection on expressionless facial image
EP01954934A EP1303842A2 (en) 2000-07-24 2001-07-24 Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US22028800P 2000-07-24 2000-07-24
US60/220,288 2000-07-24

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WO2002009038A2 true WO2002009038A2 (en) 2002-01-31
WO2002009038A3 WO2002009038A3 (en) 2002-06-27

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JP (1) JP2004505353A (en)
KR (1) KR100827939B1 (en)
AU (2) AU7714801A (en)
WO (1) WO2002009038A2 (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170040692A (en) 2015-10-05 2017-04-13 (주)감성과학연구센터 Method for extracting information of facial movement based on Action Unit
KR20170040693A (en) 2015-10-05 2017-04-13 (주)감성과학연구센터 Method for extracting Emotional Expression information based on Action Unit

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6031539A (en) * 1997-03-10 2000-02-29 Digital Equipment Corporation Facial image method and apparatus for semi-automatically mapping a face on to a wireframe topology
KR100530812B1 (en) * 1998-04-13 2005-11-28 네브엔지니어링 인코포레이티드 Wavelet-based facial motion capture for avatar animation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170040692A (en) 2015-10-05 2017-04-13 (주)감성과학연구센터 Method for extracting information of facial movement based on Action Unit
KR20170040693A (en) 2015-10-05 2017-04-13 (주)감성과학연구센터 Method for extracting Emotional Expression information based on Action Unit

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EP1303842A2 (en) 2003-04-23
AU2001277148B2 (en) 2007-09-20
WO2002009038A3 (en) 2002-06-27
JP2004505353A (en) 2004-02-19
AU7714801A (en) 2002-02-05
KR100827939B1 (en) 2008-05-13
KR20030041131A (en) 2003-05-23

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