Pa#ern
Recogni-on
and
Applica-ons
Lab
University
of
Cagliari,
Italy
Department
of
Electrical
and
Electronic
Engineering
Sparse Support Faces
Ba#sta
Biggio,
Marco
Melis,
Giorgio
Fumera,
Fabio
Roli
Dept.
Of
Electrical
and
Electronic
Engineering
University
of
Cagliari,
Italy
Phuket,
Thailand,
May
19-­‐22,
2015
ICB
2015

http://pralab.diee.unica.it
Template-based Face Verification
2
gc ≥ϑc
genuine
impostor
true
false
s(x,tc
i
){ }i=1
p
Matcher
s(⋅,⋅)
Fusion
rule
gc (x)xFeature
extrac-on
Verifica-on
is
based
on
how
similar
the
submi#ed
image
is
to
the
client’s
templates
Client-­‐specific
one-­‐class
classifica:on
mean gc (x) =
1
p
s(x,tc
i
)
i=1
p
∑
gc (x) = max
i=1,…,p
s(x,tc
i
)max
Claimed
Iden-ty
tc
1
, …, tc
p
{ }
Claimed
iden-ty’s
templates

http://pralab.diee.unica.it
Cohort-based Face Verification
3
Verifica-on
is
based
on
how
similar
the
submi#ed
image
is
to
the
client’s
templates
and
on
how
different
it
is
from
the
cohorts’
templates
Client-­‐specific
two-­‐class
classifica:on
(one-­‐vs-­‐all)
gc ≥ϑc
genuine
impostor
true
false
s(x,tc
i
){ }i=1
n
Matcher
s(⋅,⋅)
Fusion
rule
gc (x)xFeature
extrac-on
tc
1
, …, tc
p
{ }
Claimed
iden-ty’s
templates
Cohorts
tc
p+1
, …, tc
n
{ }
Claimed
Iden-ty

http://pralab.diee.unica.it
Cohort-based Fusion Rules
• Cohort selection is heuristically driven
– e.g., selection of the closest cohorts to the client’s templates
• Cohort-based fusion rules are also based on heuristics
– Test-normalization
[Auckenthaler et al., DSP 2000]
– Aggarwal’s max rule
[Aggarwal et al., CVPR-W 2006]
4
gc (x) =
1
σc (x)
1
p
s(x,tc
i
)
i=1
p
∑ −µc (x)
#
$
%
&
'
(
gc (x) =
max
i=1,…,p
s(x,tc
i
)
max
j=p+1,…,n
s(x,tc
j
)

http://pralab.diee.unica.it
Open Issues
• Fusion rules and cohort selection are based on heuristics
– No guarantees of optimality in terms of verification error
• Our goal: to design a procedure to optimally select the
reference templates and the fusion rule
– Optimal in the sense that it minimizes verification error (FRR and FAR)
• Underlying idea: to consider face verification as a two-class
classification problem in similarity space
5

http://pralab.diee.unica.it
s(x, )
s(x, )
Face Verification in Similarity Space
• The matching function maps faces onto a similarity space
– How to design an optimal decision function in this space?
6
?

http://pralab.diee.unica.it
Support Face Machines (SFMs)
• We learn a two-class SVM for each client
– using the matching score as the kernel function
– genuine client y=+1, impostors y=-1
• SVM minimizes the classification error (optimal in that sense)
– FRR and FAR in our case
• The fusion rule is a linear combination of matching scores
• The templates are automatically selected for each client
– support vectors à support faces
7
gc (x) = αis(x,tc
i
)
i
∑ − αjs(x,tc
j
)
j
∑ + b

http://pralab.diee.unica.it
Support Face Machines (SFMs)
8
s(x, )
s(x, )
• Maximum-margin classifiers
gc (x) = αis(x,tc
i
)
i
∑ − αjs(x,tc
j
)
j
∑ + b

http://pralab.diee.unica.it
Sparse Support Faces
• Open issue: SFMs require too many support faces
– Number of support faces scales linearly with training set size
• Our goal: to learn a much sparser combination of match scores
• by jointly optimizing the weighting coefficients and support faces:
9
hc (x) = βis(x, zc
k
)+ b
k=1
m
∑ , m << n
min
β,z
Ω β, z( )=
1
n
uk gc (xk )− hc (xk )( )
2
+ λβT
β
i=1
n
∑

http://pralab.diee.unica.it
z-­‐step
Sparse Support Faces
10
SFM with 12 support faces
−5 0 5
−5
0
5
−5
0
5
SSFM with 4 virtual faces
−5 0 5
−5
0
5
−5
0
5
β-­‐step
Solu:on
algorithm
is
an
itera-ve
two-­‐step
procedure:
If s(x,z) is not differentiable or
analytically given, gradient
can be approximated

http://pralab.diee.unica.it
0.5 1 2 5 10
0
5
10
15
20 AT&T − RBF Kernel
FAR (%)
FRR(%)
mean (5)
max (5)
t−norm (10)
aggarwal−max (10)
SFM (37.5 ± 3.8)
SFM−sel (10)
SFM−red (2)
SSFM (2)
Experiments
11
Datasets:
AT&T (40 clients, 10
images each)
BioID (23 clients,
1,521 images)
Matcher:
PCA+RBF kernel
(exact gradient)
5 repetitions,
different clients in
TR/TS splits
TR: 5 images/client
0.5 1 2 5 10
0
10
20
30
40 BioID − RBF Kernel
FAR (%)
FRR(%)
mean (5)
max (5)
t−norm (10)
aggarwal−max (10)
SFM (23.9 ± 2.7)
SFM−sel (10)
SFM−red (2)
SSFM (2)

http://pralab.diee.unica.it
Experiments
12
0.5 1 2 5 10
0
10
20
30
40 BioID − EBGM
FAR (%)
FRR(%)
mean (5)
max (5)
t−norm (10)
aggarwal−max (10)
SFM (15.0 ± 2.6)
SFM−sel (5)
SFM−red (5)
SSFM (5)
0.5 1 2 5 10
0
5
10
15
20 AT&T − EBGM
FAR (%)
FRR(%)
mean (5)
max (5)
t−norm (10)
aggarwal−max (10)
SFM (19.5 ± 3.0)
SFM−sel (5)
SFM−red (5)
SSFM (5)
Datasets:
AT&T (40 clients, 10
images each)
BioID (23 clients,
1,521 images)
Matcher:
EBGM
(approx. gradient)
5 repetitions,
different clients in
TR/TS splits
TR: 5 images/client

http://pralab.diee.unica.it
From Support Faces to Sparse Support Faces
• A client’s gallery of 17 support faces (and weights) reduced to 5
virtual templates by our sparse support face machine
– Dataset: BioID
– Matching algorithm: EBGM
13
4.040 2.854 −0.997 −3.525 −2.208

http://pralab.diee.unica.it
Conclusions and Future Research Directions
• Sparse support face machines:
– reduce computational time and storing requirements during
verification without affecting verification accuracy
– by jointly learning an optimal combination of matching scores, and a
corresponding sparse set of virtual support faces
• No explicit feature representation is required
– Matching algorithm exploited as kernel function
– Virtual templates created exploiting approximations of its gradient
• Future work
– Fingerprint verification
– Identification setting
• Joint reduction of virtual templates for each client-specific classifier
14

http://pralab.diee.unica.it
?
Any questions
Thanks
for
your
a#en-on!
15
Code available at: http://pralab.diee.unica.it/en/SSFCodeProject