fast face detection sami romdhani phil torr bernhard schölkopf andrew blake mike tipping
TRANSCRIPT
Fast Face Detection
Sami Romdhani Phil Torr
Bernhard SchölkopfAndrew BlakeMike Tipping
Menu
Previous Work
Support Vector Machine
Sequential Evaluation
Incremental Training
Results
Conclusion
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
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
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
Support Vector Machines (Vapnik, 1995)
Support Vectors :
SVM Training
1 2 3 4 5 i 1i 2i 3i
…
Training
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
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 :
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
Sequential Evaluation Example: Original SVM
0 % training error, 31 Support Vectors
Sequential Evaluation Example
41.7 % training error, 1 Reduced Vectors
Sequential Evaluation Example
36.7 % training error, 2 Reduced Vectors
Sequential Evaluation Example
21.7 % training error, 3 Reduced Vectors
Sequential Evaluation Example
5 % training error, 4 Reduced Vectors
Sequential Evaluation Example
0 % training error, 9 Reduced Vectors
Sequential Evaluation Example
0 % training error, 13 Reduced Vectors
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
First filter : 19.8 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
Filter 10 : 0.74 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
Filter 20 : 0.06 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
Filter 30 : 0.01 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
Filter 70 : 0.007 % patches remaining
1280x1024 image, 11 subsampling levels, 80sAverage number of filter per patch : 6.7
Incremental Training
Original Training Set
SVM Training
New Images
Detection with very low thresholds
Detected Patches
Support Vectors
Pre-Processing
We shift pre-processing to training time, instead of detection time
(Rowley et al. 1998)
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 %
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
:),(
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