ece738 advanced image processing face recognition by elastic bunch graph matching ieee trans. pami,...
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![Page 1: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/1.jpg)
ECE738 Advanced Image Processing
Face Recognition by Elastic Bunch Graph Matching
IEEE Trans. PAMI, July 1997
![Page 2: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/2.jpg)
(C) 2005 by Yu Hen Hu 2ECE738 Advanced Image Processing
![Page 3: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/3.jpg)
(C) 2005 by Yu Hen Hu 3ECE738 Advanced Image Processing
![Page 4: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/4.jpg)
(C) 2005 by Yu Hen Hu 4ECE738 Advanced Image Processing
Gabor Transform
• Gabor Function
2 22 20 0
0 0
( , ) exp(
exp 2
G x y x x a y y b
j u x x v y y
Daugman, IEEE Trans. ASSP July 1988
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(C) 2005 by Yu Hen Hu 5ECE738 Advanced Image Processing
Gabor Wavelet Transform
An implementation of Gabor transform
Gaussian envelop width = 2Last term in complex sinusoids removes DC in the kernel
5 level spatial frequency from 4 to 16 pixels in an 128 x 128 image, 8 orientations
2 2 2 2 21 1 2 2
2 2
2
( )( ) exp
2
exp( ) exp2
k k x k xx
ik x
Daugman, IEEE Trans. ASSP July 1988
![Page 6: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/6.jpg)
(C) 2005 by Yu Hen Hu 6ECE738 Advanced Image Processing
Jeta set of 40 (5 spatial frequency, 8 orientations) complex Gabor wavelet coefficients for one image point.
J = [a1, a2, …, a40]
Similarity between jets:
d is the displacement of pixels: needs to be estimated.kj: spatial wave vector
' '
, ' ' '
cos, '
'
Ta
jj j j jj
S J J J J J J
a a d kS J J
J J
Fig. 1. Similarities Sa(J,J’) (dashed line) and S(J,J’) (solid line) with J’ taken from the left eye of a face, and J taken from pixel positions of the same horizontal line. The dotted line shows the estimated displacement d (divided by eight to fit the ordinate range). The right eye is 24 pixels away from the left eye, generating a local maximum for both similarity functions and zero displacement close to dx = -24.
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(C) 2005 by Yu Hen Hu 7ECE738 Advanced Image Processing
Face Graph
• Facial fiducial points– Pupil, tip of mouth, etc.
• Face graph– Nodes at fiducial pts.– Un-directed graph– Object-adaptive– The structure of graph is the
same for each face– Fitting a face image to a face
graph is done automatically – Some nodes may be
undefined due to occlusion. Hence, association of nodes of different face graphs may need to be done manually.
• Bunch– A set of Jets all asso with
the same fiducial pt.– e.g. an eye Jet may consists
of different types of eyes: open, closed, male, female, etc.
• Face bunch graph (FBG): – Same as a face graph,
except each node consists of a jet bunch rather than a jet
![Page 8: ECE738 Advanced Image Processing Face Recognition by Elastic Bunch Graph Matching IEEE Trans. PAMI, July 1997](https://reader035.vdocuments.mx/reader035/viewer/2022072005/56649cdb5503460f949a5abc/html5/thumbnails/8.jpg)
(C) 2005 by Yu Hen Hu 8ECE738 Advanced Image Processing
Face Bunch Graph
• Has the same structure as individual face graph
– Each node labeled with a bunch of jets
– Each edge labeled with average distance between corresponding nodes in face samples
• Given a new face, an elastic bunch graph matching (EBGM) method selects the best fitting jets (local experts) from the bunch dedicated to each node in the face bunch graph.
/B Bme emx x M
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(C) 2005 by Yu Hen Hu 9ECE738 Advanced Image Processing
Elastic Bunch Graph Matching
Graph similarity measure
: weighting factor
• Initially, manually generate a few FGs to create a FBG
• Heuristic algorithm to find the image graph that maximizes the similarity:– Coarse scan of image using
jets to detect face
– Varying sizes and aspect ratio of FBG to adapt to right format of face.
– Finally, all nodes are moved locally to maximize SB.
2
2
1( , ) max ,
: displacement on edge e
: jet at node n
I I BmB n n
mn
I Be e
Bee
Ie
In
S G B S J JN
x x
E x
x
J
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(C) 2005 by Yu Hen Hu 10ECE738 Advanced Image Processing
Results