IMPROVEMENT ON THE INDIVIDUAL RECOGNITION
SYSTEM WITH WRITING PRESSURE BASED ON RBF
Lina Mi, Fumiaki Takeda
Department of Intelligent Mechanical System, Kochi University of Technology
185 Miyanokuchi, Tosayamada-cyo, Kami-gun, Kochi, Japan
Keywords: Writing pressure, RBF, Gaussian function, Neuro-template
Abstract: In our previous research work, an individual re
cognition system with writing pressure employing neuro-
template of multiplayer feedforward network with sigmoid function has been developed. Although this
system was effective on recognition for known registrant, its rejection capability for counterfeit signature
was not good enough for commercial application. In this paper, a new activation function was proposed to
improve the rejection performance of the system for counterfeit signature on the premise of ensuring the
recognition performance for known signature. The experiment results showed that compared with original
system the proposed activation function was seemed to be effective to improve the rejection capability of
the system for counterfeit signature with keeping the recognition capability for known signature satisfied.
1 INTRODUCTION
In recently years, biometrics information such as
fingerprint, iris is gradually applied into the
individual recognition systems replacing the security
material such as licence or security information such
as personal identification number (PIN), password.
Compared with conventional recognition systems,
high security is one of superiority of recognition
system with biometrics information because of
difficulty of imitation or copy for biometrics
information.
Every person’s signature has unique character in
ter
ms of rhythm and force employed during
signature. Writing pressure data detected
dynamically from signature procedure can
characterize individual signature if being properly
processed. Being friendly biometrics information,
writing pressure has little spirit resistance compared
with other biometrics information such as fingerprint
and iris. Furthermore, unlike handwriting, writing
pressure data is dynamic signal and invisible to users,
that contributes to more difficulties of imitation and
copy, therefore writing pressure has higher security
as personal information for individual recognition
than handwriting. In our research work, writing
pressure has been successfully employed to develop
individual recognition system.
In our system, neuro-template of multiplayer
feed
forward neural network (MFNN) with sigmoid
as activation function of hidden and output layer was
used to construct kernel part of the system. The
experiments showed that the system with sigmoid
function was effective on recognition for the patterns
having been learned (known registrant), but the
rejection capability for counterfeit writing which is
from imitating legitimate registrant’s signature by
intruders was not good. To solve the previous
problem, Gaussian function, which is one of radial
basic function (RBF), was proposed as activation
function of the NN in this paper. In the experiment,
both recognition and rejection performance of the
system with Gaussian function were compared with
that of original system with sigmoid function, and
the results showed that the proposed method has
effectively improved the rejection performance of
the system for counterfeit signature with keeping
recognition capability for known pattern satisfied.
2 BASIC STRUCTURE OF THE
INDIVIDUAL RECOGNITION
SYSTEM
The individual recognition system with writing
pressure is composed of hardware section and
software section.
157
Mi L. and Takeda F. (2005).
IMPROVEMENT ON THE INDIVIDUAL RECOGNITION SYSTEM WITH WRITING PRESSURE BASED ON RBF.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 157-162
DOI: 10.5220/0002511401570162
Copyright
c
SciTePress
2.1 Hardware Construction of the
System
The hardware section of system, which is
constructed by the electronic pen, data collection
box and personal computer, is in charge of data
detection and transmission. Figure 1 illustrated the
hardware structure of the system. In inner of
electronic pen, there is a pressure sensor with 0.1g
writing pressure resolution and 4ms time resolution,
that makes high resolution of the extraction of
writing pressure data possible. In the hardware
section, writing pressure data are detected by the
electronic pen, and then loaded to PC via the data
collection box. After that, the registration and
recognition procedure are executed on these data
based on the Neuro-template.
2.2 Software Construction of the
System
This section completes most function of the system
such as data preprocess, new registration, registrant
recognition and result display etc, It is composed of
registration part and recognition part which are
independent each other. Both of two parts are based
on neural template of MFNNs. Registration
subsystem is in charge of recruiting new legitimate
registrant: generating and training one neuro-
template for him (or her). Recognition subsystem
has role of recognizing the identity of the user who
entries the system from all the candidate registrants
who have registered on the system legitimately by
matching this user’s signature with all of existing
neuro-templates and then evaluating output of each
template.
3 PREPROCESSING
The writing pressure data detected by hardware
section can not be fed into neural network directly
because of too much redundant data. Therefore the
preprocessing of writing pressure data is
indispensable and crucial for system. There are three
steps for the data preprocessing, and in each step
different treatment is implemented and different data
are obtained.
Registration Based on NN
Recognition Based on NN
First, after three times of signature given by
registrant, the writing pressure data set with more
than 1000 data are transformed into normal data
with number of about 300 by Moving Average
method. During this process, the individual feature
of pressure data is extracted and data scale is
compressed greatly.
Second, validity check is implemented on
normal data obtained in first step basing on two
factors: correlation coefficient
R
and statistical
distance
.
D
The correlation coefficient
R
between Data1 and
Data2 is calculated as following equation:
2
)2(
1
2
)(1
1
2
))1((
2
(Data1(s))
)2()1(
1
))(2)(1(
dAveS
S
s
sData
S
s
dAveS
dAvedAve
S
s
sDatasData
R
∗−
∑
=
∑
=
∗−
∗−
∑
=
∗
=
⎯ (1)
Here,
and are average of
Data1 set and Data2 set respectively with assuming
that there are S elements in each data set. The value
of
)1(dAve
)2(dAve
R
varies between –1 and 1. More near 1 R is,
more closely two data correlate.
Statistical distance (Euclidean distance) between
Data1 and Data2 is calculated as following equation:
∑
=
−=
S
s
sDatasDataD
1
2
))(2)(1(
⎯(2)
The deviation between each two of three writing
pressure data are calculated according to their
correlation coefficient and statistical distance, if
deviation is more than upper limit, the
corresponding signature data are treated as abnormal
and registrant is asked for re-signature.
In this system, only three writing pressure data
set as enforce data are not enough for purposive
pattern learning of Neuro-template, furthermore the
inhibit data for non-purposive pattern are also
necessary. So not only purposive simulative data
(enforce data) but also non-purposive simulative
data (inhibit data) are constructed based on the
detected writing pressure data and the corresponding
normal data are then constructed.
Last, In order to obtain 50 slab data for Neuro-
template from 300 normal data, mask data are made
by comparison on distribution of standard error and
average value between enforce normal data and
inhibit data, then 50 slab data for the Neuro-template
are obtained by filtering middle data with the mask
data.
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158
hidden laye
r
4 ALGORITHM
purposive pattern
For traditional MFNN, when recruiting new pattern,
the whole neural network will have to be
restructured and retrained, that would lead to
expensive cost of computation and time. In order to
eliminate the restriction on the number of registrants
in the recognition system and simplify the
recruitment of new pattern, neural template
matching method showed as Figure 2 was proposed
and applied into this individual recognition system.
In this method, one neural template corresponds to
one pattern (registrant). When new registration is
completed, a new neural template is constructed and
trained for this new registrant, other neuro-templates
generated previously will remain untouched unless
mis-outputs are caused by signature of new
registration in these templates. That is to say instead
of training all of templates, only the training for new
template and the retraining for the existing templates
in which the mis-recognition occurred are involved
in the procedure of new registration. Therefore the
cost of calculation and time for new registration is
greatly decreased and registration procedure is
simplified.
non-purposive pattern
In the neural template matching method, each
neuro-template is constructed by three layers
feedforward neural network with structure of 50×35
×2 which is shown in Figure 3, one of two output
layer units corresponds to purposive pattern
(purposive registrant) and the other to non-purposive
one (non-purposive registrant). The function of each
neuro-template is evaluating whether the input data
is the purposive pattern of this template or not rather
than deciding which pattern the input data is from all
of templates. Recognizing the pattern of input
signature from existing templates is completed by
template matching procedure. The experiment
results showed that this strategy was effective for
individual recognition system.
In the previous research work, sigmoid function
was employed as activation function of neuro-
template in the individual recognition system. The
simulation experiments have shown that the system
was effective on classifying the signatures that
belong to known patterns (the average recognition
rate was over 95%), however rejection performance
for counterfeit signature (always unknown pattern)
was not satisfying. This problem caused by the
limitation of the MFNNs: the pattern space is
divided up into several areas corresponding to the
patterns that have been learned in a specific case and
the networks may be trained to have high accuracy
in classifying patterns for a set of known categories,
so it can successfully recognize the signature of
registrant whose pattern have been learned. But for
any pattern which is out of known categories, it is
also very likely to be classified as one of known
categories by MFNNs. That leads to the poor
rejection capability for counterfeit signatures.
To improve the rejection performance of the
system for counterfeit signature, Gaussian function,
which is one of RBF, was proposed as activation
function of neural unites in this paper. Instead of
dividing up the pattern space as MFNNs do, neuro-
template with Gaussian function learns the pattern
probability density. Therefore, when an out-of-
category pattern is evaluated, it is likely to be
recognized as an unknown category by neuro-
template with Gaussian function. Then the rejection
performance of the system for counterfeit signature
is expected to be improved. The expression of
Gaussian activation function of hidden and output
layers neuron is described as following equation:
)2exp()(
2
2
σ
c
xxxf −−= ⎯ (3)
Where
and
c
x
σ
are the centre vector and the
width parameter of Gaussian function respectively.
Both of them have direct effect on the convergence
and recognition capability of the individual
recognition system.
In the neuro-template training procedure,
improved Back Propagation (BP) is employed to
modify the weights between neighbour layer
neurons and corresponding expression was described
as following:
Figure 3: Structure of neuro-template
output layer Input layer
p
urpos
i
ve
p
attern A
Template A
Template B
Template N
Check
of
value
Slab
value o
f
pen
pressure
non-purpos
i
ve
p
attern A
Figure 2: Construction of Neuro-template Matching Mechod
IMPROVEMENT ON THE INDIVIDUAL RECOGNITION SYSTEM WITH WRITING PRESSURE BASED ON RBF
159
)2()1(
)(
)( −∆⋅+−∆⋅+
∂
∂
⋅−=∆ tWtW
tw
E
tW
βαη
⎯ (4)
Where
η
,
α
,
β
are the learning rate, momentum
coefficient, and oscillation coefficient respectively.
The momentum item contributes to accelerating
convergence and the oscillation item has function of
escaping local minimum
. The neuro-templates are
trained with learning data set till the cost functions
meet the requirements of minimal error.
Figure 4: Registrant Signature and Corresponding
Writing Pressure data
5 EXPERIMENTS
In the research work of this paper, a series of
simulation experiments were made on both of
individual recognition system with Gaussian
function and that with sigmoid function for the
purpose of comparison. The conditions for neuro-
template learning are listed in table 1.
Table 1: Conditions of neuro-template learning
Generally the registration and recognition of the
system are operated on-line, but in order to ensure
the identical writing pressure data for neuro-
templates with different activation function, the
registrant signs on the signature sheet under the
same condition and off-line registration and
evaluation is implemented with data file in the
experiment. One of registrant signature and
corresponding writing pressure data are shown as
Figure 4.
To demonstrate average performance of the
system for any template, three registrants (labelled
as A, B, C) were selected randomly, 53 signature
samples were extracted from each purposive
registrant as purposive signatures and any three of
these samples were employed for neuro-template
learning and the left for evaluation. To evaluate
counterfeit rejection performance of the system, 90
samples were extracted as counterfeit signatures
from any five people (except A, B, C) with 18
samples per person for each registrants and the three
groups of five people who give the counterfeit
signatures were different each other.
The evaluation items in the experiments are
recognition ratio and rejection ratio. Recognition
ratio presents the percentage of signatures being
successfully recognized to all purposive signatures
(50). Rejection ratio for counterfeit signatures
presents the percentage of signatures being
successfully rejected as unknown pattern to all
counterfeit signatures (90). Assuming having three
registrants (A, B and C) in the system, taking
registrant A as purposive registrant for example,
signature (purposive signatures of A or counterfeit
signatures of A) is fed to each template and
evaluated by respective template. All possible results
of the system were illustrated in Table 2. Note that
output of the system will be one of these three
registrants or none of them (briefly N).
Maximum iteration number 1000
Learning rate 0.05
Momentum coefficient 0.95
Oscillation coefficient -0.1
Error threshold 0.0001
Value of (only for Gaussian)
σ
0.3~1.0
Table 2: All Possible Results of Experiments
Evaluated signature
Purposive
signature
Counterfeit
signature
Source of signature A
Anyone except
A, B and C
Ideal Results A N
Success A N
N A
Possible
results
of
system
Failure
B or C B or C
In the previous table, the failure cases in which
purposive signature of A is mis-recognized as B or C
and the counterfeit signature for A is mis-recognized
as B or C can easily be overcome by our system
because of significant difference among the
signatures of the respective registrant.
Based on the previous condition and the sampled
data (including purposive and counterfeit), neuro-
templates with Gaussian Function and that with
sigmoid function are trained with the sequence of A,
B, C and evaluated respectively, and the experiment
results are presented in Table 3 and Table 4.
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Table 3: Recognition ratio for purposive signatures
(ideal value: 100%)
Registrant Sigmoid Gaussian Difference
A 96% 96% -0%
B 98% 96% -2%
C 96% 92% -4%
Average 96.67% 94.67% -2%
Table 4: Rejection ratio for counterfeit signatures
(ideal value: 100%)
Registrant Sigmoid Gaussian Difference
A 64.44% 82.22% +17.78%
B 85.56% 90.0% +4.44%
C 71.33% 88.89 % +17.56%
Average 73.77% 87.04% +13.27%
The item of ‘Difference’ in above tables is for
easy discrimination on the performance change
between two systems. The minus sign (-) indicated
deterioration of the performance of system with
Gaussian function compared with that with sigmoid
function and the positive sign meant the
improvement. According to table 3 and table 4,
though the recognition capability of system with
Gaussian function decreased slightly (average –2%),
its rejection capability for counterfeit signatures was
improved greatly with average value of 13.27%.
This result suggested that the neuro-template with
Gaussian function proposed in this paper tended to
be effective on improving the rejection capability of
system for the counterfeit signatures and at the same
time keeping the recognition capability for
purposive registrant signatures satisfied.
As mentioned in section four, the recruitment of
new registrant in the system will lead to retraining
on the exiting templates in which mis-outputs are
caused by signatures for new register. That means
the neuro-template in the system is likely to be
influenced by the templates registered later. To
investigate the mutual influence of different neuro-
templates, more experiments were conducted on the
system with different number and different sequence
of templates. Registrant A and B in previous
experiments were selected for the purpose of
convenience. In these experiments, when registrant
B registered after A, retraining on template A was
induced by recruitment of B, however no retraining
on template B in the case of registrant A registering
after B. Note that any templates except A and B was
not included in this system.
First, the performance of template A with
sigmoid function and that with Gaussian function
under different situation were investigated and the
corresponding results were demonstrated in Table 5
and Table 6 respectively.
Table 5: Performance of template A with sigmoid function
under different situation (ideal value: 100%)
Performance
Order
of registrants
Recognition Rejection
A only 96% 57.78%
A, B 96% 64.44%
B, A 96% 57.58 %
Table 6: Performance of template A with Gaussian
function under different situation (ideal value: 100%)
Performance
Order
of registrants
Recognition Rejection
A only 96% 68.89%
A, B 96% 82.22%
B, A 96% 68.89 %
Table 7 and Table 8 showed the performance of
template B with sigmoid function and that with
Gaussian function under different cases respectively.
Table 7: Performance of template B with sigmoid function
under different situation (ideal value: 100%)
Performance
Order
of registrants
Recognition Rejection
B only 98% 85.56%
B, A 98% 85.56%
A, B 98% 85.56 %
Table 8: Performance of Template B with Gaussian
Function under Different Situation (Ideal Value: 100%)
Performance
Order
of registrant
Recognition Rejection
B only 96% 90.0%
B, A 96% 90.0%
A, B 96% 90.0 %
As can be seen from above tables, the
recognition performance of both template A and B
with different activation function were not
IMPROVEMENT ON THE INDIVIDUAL RECOGNITION SYSTEM WITH WRITING PRESSURE BASED ON RBF
161
influenced by the register sequence, that indicated
that the recognition performance of the system was
not affected by increase of templates. While for
counterfeit rejection capability, there were two cases:
one case was that new register led to retraining on
the templates registered previously (Table 5 and
Table 6); the other case was that no retraining was
resulted by new register (Table 7 and Table 8).
According to Table 5 and Table 6, in both two
systems with different activation function, the
rejection capability of template A was enhanced by
new register of B and this improvement was
especially remarkable with employment of Gaussian
function. From Table 7 and Table 8, the rejection
performance of template B kept untouched because
no retraining was caused by recruitment of template
A. these experiment results showed that recruitment
of new templates was helpful to decreasing the
possibility of mis-recognition on counterfeit
signatures in one template.
The results in Table 3 and 4 involved the
influence among templates. Excluding the influence
among templates, the rejection performance of
templates with different activation function was
investigated and corresponding results were listed in
Table 9. Note that in each case, only one registrant
was involved in the system.
Table 9: Rejection performance of template with
different activation function (ideal value: 100%)
Function
Registrant
Sigmoid Gaussian Difference
A only 57.78% 68.89% +11.11%
B only 85.56% 90.0% +4.44%
C only 73.77% 87.04 % +13.27%
Average 72.37% 81.97% +9.60%
It can be seen that even without the help of
favourable influence of templates, Gaussian function
was still effective on improving the rejection
capability of template.
During experiments, the width parameter σ was
found to have great influence on both recognition
capability and counterfeit rejection capability of the
system with Gaussian function. We are engaging in
developing automatic optimisation method of σ for
each registrant’s neuro-template.
6 CONCLUSION
In this paper, both of the construction and algorithm
of the individual recognition system with writing
pressure were firstly described. Then, Gaussian
function, which is one of RBF, was proposed as
activation function of neuro-template to improve the
rejection capability of the system for counterfeit
signatures. Furthermore the influence among neuro-
templates was investigated in this paper. The
experiments results suggested that the influence
among templates was favourable for rejection
capability of the system, and more importantly the
experiment results shown that Gaussian function
combined with neuro-template was seemed to be
very effective in improving rejection performance of
the system for counterfeit signatures on premise of
ensuring the recognition performance satisfied.
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