Fast Face Detection Sami Romdhani Phil Torr Bernhard Schölkopf Andrew Blake Mike Tipping.
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Transcript of Fast Face Detection Sami Romdhani Phil Torr Bernhard Schölkopf Andrew Blake Mike Tipping.
![Page 1: Fast Face Detection Sami Romdhani Phil Torr Bernhard Schölkopf Andrew Blake Mike Tipping.](https://reader036.fdocuments.us/reader036/viewer/2022062619/5515ea41550346dd6f8b511d/html5/thumbnails/1.jpg)
Fast Face Detection
Sami Romdhani Phil Torr
Bernhard SchölkopfAndrew BlakeMike Tipping
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Menu
Previous Work
Support Vector Machine
Sequential Evaluation
Incremental Training
Results
Conclusion
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2. Search
Face detection = localising faces in imagesis possible, but slow
Rowley
ClassificationMachine Face
Non-face
1.
825,880 patchesComputationally intensive
7
1
)1(27.0450600l
l
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Improving Speed : Rowley’s way
Instead of :
Learn on :
Rowley’s Detection rate decreases to 75%, speed : 5 to 7 s.
20
20
30
30
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Improving Speed : our way
Idea : most of the patches can be easily discriminated For these, classification must be fast
Hence, classification complexity must be variable :classifier = set of cheap filters of increasing complexity
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Support Vector Machines (Vapnik, 1995)
Support Vectors :
SVM Training
1 2 3 4 5 i 1i 2i 3i
…
Training
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1 2 3 4 5 i 1i 2i 3i
…
D D D D D D D D D
Output
2. ClassificationIs this path a face ?
Support Vector Machines (Vapnik, 1995)
> T Face<= T Non-Face
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Reduced Set Vector Post-Processing
xN
iii xw
1
)(
zN
iii zw
1
)(* xz NN with
2
,*min ww
iiz
by an iterative procedure
Find which minimise11, z2
11 )(zw
Find which minimise22 , z 2
2211 )()( zzw
… (Schölkopf et al. 1999):
Reduced Set Vectors :
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Sequential Evaluation
Is patch a face ?
)( 111 zw 1111 ),( Txzky < 0 classified as a non-face>= 0 continue
)()( 22112 zzw 222112 ),(),( Txzkxzky < 0 classified as a non-face>= 0 continue
…
x
100
1100 )(
iii zw
100
100100 ),(i
ii Txzky
< 0 classified as a non-face>= 0 use the full SVM
xN
iii bxxky ),(
< 0 classified as a non-face>= 0 classified as a face
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Sequential Evaluation Example: Original SVM
0 % training error, 31 Support Vectors
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Sequential Evaluation Example
41.7 % training error, 1 Reduced Vectors
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Sequential Evaluation Example
36.7 % training error, 2 Reduced Vectors
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Sequential Evaluation Example
21.7 % training error, 3 Reduced Vectors
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Sequential Evaluation Example
5 % training error, 4 Reduced Vectors
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Sequential Evaluation Example
0 % training error, 9 Reduced Vectors
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Sequential Evaluation Example
0 % training error, 13 Reduced Vectors
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Rejection Example
F1 : 3.7%
F10 : 0.72%
f20 : 0.003%
f30 : 0.00005%
312x400 image, 7 subsampling level, 10.4 s.Average number of filters per patch : 1.51
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First filter : 19.8 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
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Filter 10 : 0.74 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
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Filter 20 : 0.06 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
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Filter 30 : 0.01 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
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Filter 70 : 0.007 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
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Incremental Training
Original Training Set
SVM Training
New Images
Detection with very low thresholds
Detected Patches
Support Vectors
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Pre-Processing
We shift pre-processing to training time, instead of detection time
(Rowley et al. 1998)
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Results
Detection rate False Positive
Rowley 1 - best detection 91.7 % 0.0006 %
Rowley 2 - lowest FP 77.9 % 2.4*10-6 %
MSR Cam 1 - best detect. 80.6 % 0.0012 %
MSR Cam 2 - lowest FP 57.8 % 0.00004 %
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Future Work• Investigate fast preprocessing at detection time
• Change the Reduced Set Vector algorithm so that it takes the data into account :Now : Future :
• Change the kernel so that it takes info about face variation into account :Now : Future :
• Try Tipping’s Relevance VM instead of Reduced VM
• Colour
• Once a face is detected, use that prior information
• Recode by a good SDE
2
,*min ww
iiz
j
jjz
xwxwii
2
,)(*,)(,min
22:),( xx
i
i
exxk
xxi
iexxk1
:),(
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Conclusion
New Fast Face Detection algorithm :
• Based on a early rejection classification
• Speed dependent on the complexity of the data
• Accuracy-wise, not yet on a par with state of the art, but promising enough