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AAM based Face Tracking with Temporal Matching and Face
SegmentationDalong Du
Outline
• Author Introduction• AAM Introduction• Abstract• Method and Theory• Experiment
Author Introduction
• Mingcai Zhou– Institute of Automation Chinese Academy of
Sciences
• Lin Liang– Microsoft Research Asia–
Author Introduction
• Jian Sun– Microsoft Research Asia• joined in July, 2003.
– Educational background • BS degree, MS degree and Ph.D degree from Xian
Jiaotong University in 1997, 2000 and 2003– Current research interests • Interactive compute vision (user interface + vision)• Internet compute vision (large image collection + vision)• stereo matching and computational photography
Author Introduction
• Yangsheng Wang– Director of Digital Interactive
Media Lab, Institute of Automation Chinese Academy of Sciences– Educational background• BS degree, MS degree and Ph.D degree from Huazhong
University of Science and Technology
AAM Introduction
• Shape Model• Appearance (Texture) Model• AAM Model Search
AAM—Shape Model• Face Q consists of N landmark points– – The geometry information of Q decouples into two parts:
• A shape S– Shape is the geometric information
invariant to a particular class of transformations
– e.g. Or other linear or nonlinear methods
• A transformation– θ– e.g. similarity s, R, t Or
Affine or others.– Similarity
»
x u b b
θ
x = (x1,y1, … , xn, yn)T
Same shape Different shape
AAM—Shape Model
• Shape Model Building– Given a set of shapes– Align shapes into common frame• Procrustes analysis
– Estimate shape distribution p(x)• Use PCA
The aligned shapes
AAM—Shape Model
• Shape Model Building, continued– Given aligned shapes, { }– Apply PCA• Compute mean and eigenvectors of covar.
– P – First t eigenvectors of covar. matrix– b – Shape model parameters
ix
Pbxx
AAM—Texture Model
• Building Texture Models– For each example, extract texture vector
– Normalise vectors (as for eigenfaces)– Build eigen-model
Texture, g
Warp tomeanshape
ggbPgg
1b12 12 2b22
22
AAM—Texture Model
• Warp method ),( :points Control ii yx )','( :points Warped ii yx
a b
c
x 'a
'b
'c
'x
cbax ''' cbax ' 1
10 and 10
if triangle theinside is
βα
x
AAM—Texture Model
• Warp method, continued
a b
c
x
)( ab
)( ac cba
cba
acabax
)1(
)()(
cbax
1
1111yyy
xxx
cba
cba
y
x
AAM—Model Search• Find the optimal shape parameters and appearance
parameters to minimize the difference between the warped-back appearance and synthesized appearance
( , )W x p
p
( ( ))I W p
( ( , ))I W x p
map every pixel x in the model coordinate to its corresponding image point( , )W x p
0s
Computed by the inverseCompositional parameter Update technique
Abstract
• Problems– Generalization problem– images with cluttered background
• How to do?– A temporal matching constraint in AAM fitting• Enforce an inter-frame local appearance constraint
between frames
– Introduce color-based face segmentation as a soft constraint
Method and Theory• Extend basic AAM to Multi-band AAM– The texture(appearance) is a concatenation of three
texture band values• The intensity (b)• X-direction gradient strength (c)• Y-direction gradient strength (d)
Method and Theory
• Temporal Matching Constraint– Select feature points with salient local appearances at
previous frame– Optimize the shape parameters to match the local
appearances at current frame
Method and Theory
• Temporal Matching Constraint, continued
– : a set of feature points• Selected by a corner detector and some semantic points
– : the face appearance of frame t-1– : the local patch corresponding to the j-th feature
point– : the average intensity of j-th patches of
frame t-1 and t respectively
t
1tA
jR
Normalize the illuminations of two patches
Method and Theory
• Temporal Matching Constraint, continued– Add a new term to the AAM cost function
• Empirically,
Can be efficiently minimized based on inverse compositional algorithm
Method and Theory
• Temporal Matching Constraint, continued– Be resistant to global illumination changes• Match local patches
– Do not suffer from the mismatched points• Feature matching is continuously refined by updating
the shape parameters during AAM fitting
Method and Theory
• Initialize shape– Good initial parameters -> good AAM fitting– Method • Selected feature points at frame t-1• Matched feature points at frame t• Remaining feature points after main direction filter
Method and Theory
• Initialize shape, continued– M matched points– Estimate the initial shape parameters
• represents the consistency of feature
points I’s direction• is the estimated position of the point I given
the shape parameters p• are the vertex coordinate of the triangle• are the triangle coordinate
1
M
i iz
0p
Gauss-Newton algorithm
Method and Theory
• Face Segmentation Constrained AAM– Problem: AAM tends to fit the face outline to the
background edges
– Method: segment the face region using an adaptive color model and constrain AAM fitting
Method and Theory
• Formalization
– Where are the locations of the selected outline points in the model coordinate
{ }kx
Wc = 0.01
Experiments
• RI: robust initialization• TO: temporal matching constraint • FS: face segmentation
Experiments
Thank you