F ace image m apping from NIR to VIS Jie Chen Machine Vision Group ee.oulu.fi/mvg
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Transcript of F ace image m apping from NIR to VIS Jie Chen Machine Vision Group ee.oulu.fi/mvg
MACHINE VISION GROUP
Face image mapping from NIR to VIS
Jie ChenMachine Vision Group
http://www.ee.oulu.fi/mvg
MACHINE VISION GROUP
Outline
• Problem
• Methods
• Preliminary results
• Plans for next period
MACHINE VISION GROUP
Face image mapping from NIR to VIS
• Problem– NIR: Near infrared imaging– VIS: Visual light imaging
MACHINE VISION GROUP
Face image mapping from NIR to VIS
• Problem– NIR: Near infrared imaging– VIS: Visual light imaging
MACHINE VISION GROUP
Algorithm: Patches mapping Training
• Training
wf
hf
wp
hp
wo
ho
MACHINE VISION GROUP
Algorithm: Patches mapping Training
• Mapping
φi,j
1,,k
i j
MACHINE VISION GROUP
Look up the KNN
Looking up
Ddictionary of face patches and their LBP
histograms
1,1,i j
4,i j
3,i j
1,0,i j
2,1,i j
2,0,i j
0
1
1K
1, 1,K
i j
2, 1,K
i j
1,,ki j
3,i j
k
2,,k
i j
4,i j
1,,ki j
A patch of an input sample in S3
k-th nearest patch in S1
Weight of k-th nearest neighbor
A patch of an input sample in S4
Corresponding patch of in S2
MACHINE VISION GROUP
Weight computing
1 2 1, 2,1
( , ) min( , )L
i ii
H H H H
1
0
kk K
pp
3 2,, ,
ki j k i j
Looking up
Ddictionary of face patches and their LBP
histograms
MACHINE VISION GROUP
Experiments
• Setup– both S1 and S2 is composed of 300
samples.• 50 subjects, • each subject has 6 images but in
different expression (anger, disgust, fear, happiness, sadness, and surprise).
– wf =64, hf =80, wp=16, hp=16, wo =12, ho =12
– Testing:using leave-one-out and K=15.
wf
hf
wp
hp
wo
ho
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Reconstructed images
(a) Input images in NIR
24.88 23.76 27.55 26.29 26.48 27.43 21.54 21.18
(b) Reconstructed images in VIS using LBP(8,1) and the PSNR
31.89 32.11 32.11 34.41 32.11 31.68 32.18 31.08
(c) Reconstructed images in VIS using the combined Multi-resolution LBP and their PSNR
(d) Ground truth in VIS
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Multi-resolution LBP (MLBP)
(P=4,R=1) (P=8,R=1) (P=12,R=1.5) (P=16,R=2) (P=24,R=3)
29 9 4
42 29 2
55 15 6
1 0 0
1 0
1 0 0
1 0 0
8 0
32 0 0
1 2 4
8 16
32 64 128
LBP=1+8+32=41
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PNSR
2
10 1010 log 20 logI IMAX MAXPSNR
MSE MSE
1 12
0 0
1( , ) ( , )
w h
i j
MSE I i j I i jwh
2
1 21 ( , )MSE H H
2
10 10
110 log 20 logIMAX
PSNRMSE MSE
Pixel wise
LBP
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Multi-resolution LBP
, , ,( )p s p s cf
1 2 1, 2,1
( , ) min( , )L
i ii
H H H H
1
0
kk K
pp
3 2,, ,
ki j k i j
Looking up
Ddictionary of face patches and their LBP
histograms
1
, , , , ,0
( )C
p s p s c p s cc
f
MACHINE VISION GROUP
PSNR on MLBP
0 2 4 6 8 100
5
10
15
20
25
30
35
40
24. 4526. 52
29. 77
35. 13
26. 45
29. 2329. 2929. 7729. 6427. 92
PS
NR
CS
LBP
PSNR for combining multi-resolution LBP by different methods
Sum
Produ
ctM
axM
in
Med
ianM
ean
1-LBP-C
S
1-LBP
Conca
tenati
onPCA
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Plans for next period
• Training data:– Use more samples (192*10 from CASIA, a group in Beijing,
China)
• Methods:– Combine the methods proposed in the paper (A.
Hertzmann, SIGGRAPH, 2001) for better performance