A Mobile Phone with Biometric Authentication and e-Signature Support for
Dealing Secure Transactions on the Fly
R. Ricci
Informa s.r.l., Via dei Magazzini Generali 31, 00154, Rome, Italy
G. Chollet
GET-ENST, Dept. TSI, 46 rue Barrault, 75634 Paris cedex 13, France
M. V. Crispino
Nergal s.r.l., Viale B. Bardanzellu, 8, 00155, Rome, Italy
S. Jassim
Buckingham University, Hunter Street, Buckingham, MK18 1EG, United Kingdom
J. Koreman, A. Morris
Saarland University, Postfach 15 11 50, 66041 Saarbrücken, Germany
M. Olivar-Dimas
Telefónica Móviles España S.A., Plaza de la Independencia 6, 28001, Madrid, Spain
S. García-Salicetti
GET-INT, 9 rue Charles Fourier, 91011, Évry cedex, France
P. Soria-Rodríguez
Atos Origin, c/ Albarracín, 25, 28037, Madrid, Spain
Keywords: Mobile communications, multimodal biometrics, biometric authentication, electronic signature, security,
encryption, m-business.
Abstract: This article presents an overview of the SecurePhone project, with an account of the first results obtained.
SecurePhone’s primary aim is to realise a mobile phone prototype - the “SecurePhone” - in which
biometrical authentication enables users to deal secure, dependable transactions over a mobile network. The
SecurePhone is based on a commercial PDA-phone, supplemented with specific software modules and a
customised SIM card. It integrates in a single environment a number of advanced features: access to
cryptographic keys through strong multimodal biometric authentication; appending and verification of
digital signatures; real-time exchange and interactive modification of (e-signed) documents and voice
recordings. SecurePhone’s “biometric recogniser” is based on original research. A fused combination of
three different biometric methods - speaker, face and handwritten signature verification - is exploited, with
no need for dedicated hardware components. The adoption of non-intrusive, psychologically neutral
biometric techniques is expected to mitigate rejection problems that often inhibit the social use of
biometrics, and speed up the spread of e-signature technology. Successful biometric authentication grants
access to SecurePhone’s built-in e-signature services through a user-friendly interface. Special emphasis is
accorded to the definition of a trustworthy security chain model covering all aspects of system operation.
Present wireless environments are not completely
safe (Welch et al. 2003) (Torvinen, 2000). No
mobile network operator can guarantee that
confidential information (such as credit card
numbers, personal financial data, trade secrets or
business documents) can be transmitted over the air
Ricci R., Chollet G., V. Crispino M., Jassim S., Koreman J., Morris A., Olivar-Dimas M. and Soria-Rodríguez P. (2006).
THE “SECUREPHONE” - A Mobile Phone with Biometric Authentication and e-Signature Support for Dealing Secure Transactions on the Fly.
In Proceedings of the International Conference on Security and Cryptography, pages 9-16
DOI: 10.5220/0002103900090016
in a secure way. Likewise, it is often not possible to
reliably verify a user’s identity, due to the absence
of trustworthy strong authentication procedures.
Security and dependability are essential prerequisites
for the spreading, for instance, of mobile e-business
(m-business) applications, especially where legal
aspects play an essential role. In synthesis mobile
infrastructures should provide the following four
major security services:
Authentication (verification of the user’s
identity by remote).
Confidentiality (privacy)
Non-repudiation (signing in a verifiable way at
a later stage).
Integrity (sealing: during transmission and after
a signed digital agreement).
It is expected that a combination of Public Key
Infrastructure (PKI) technology and biometrics can
play a key role to enhance wireless environments
safety by ensuring identity and protecting
In this article we present an original solution,
developed in the context of the SecurePhone project
(an international project co-funded by the European
Commission started in 2004). The SecurePhone is an
innovative prototypal mobile phone platform that
gives users the possibility to authenticate by means
of a multimodal “biometric recogniser”, exchange,
modify in real time and finally e-sign and securely
transmit audio and/or text files. The biometric
recognition is based on three modalities: voice, face
and handwritten signature recognition.
In section 2 we describe SecurePhone’s main
objectives and system architecture. Section 3 briefly
presents the biometric recogniser and the method
used for score fusion. Section 4 reports preliminary
results of the project. In section 5 we present some
ideas for future developments. Conclusions are
given in section 6.
The aim of the SecurePhone project is to enable
biometrically authenticated users to send/receive
files via a mobile phone in an easy yet highly
dependable and secure way.
The typical case of use considered in the project
is that of two users (the proposer and the endorser)
who directly exchange, eventually agree upon and e-
sign a digital document (e-contract):
the proposer sends to the endorser the e-contract
- either a text or a digital audio file;
the e-contract – at least in the case of a text file -
is modified and transmitted back and forth
between the two users as many times as needed
to reach a formal agreement on its contents;
the endorser eventually e-signs the e-contract
and sends it to the proposer as an evidence of
formal acceptance of the contract terms.
Depending on the contract type, the proposer
could also be requested to e-sign the e-contract;
just before that the e-signature procedure is
initiated, the host application running on the
PDA asks the user to pass an authentication
challenge, in order to “unlock” the e-signature
private key located on the SIM card and get
access to built-in cryptographic services.
It is assumed that the private key of the
SecurePhone’s owner - needed for e-signature and
other cryptographic tasks - is safely placed on the
SecurePhone’s SIM card, which, besides supporting
normal telephonic services, also provides the
possibility of tamper-proof data storage.
The SecurePhone can also be adapted to be used
in a User/Business model, in which a single user
accesses some business service provider over a
private or public network.
In the use case described above the
authentication challenge, which gives access to the
private key stored onto the SIM, is the crucial phase
of the process. In normal practice, authentication is
done by inputting a password or a PIN. This is
considered a weak authentication modality, that is
not particularly suited for critical applications such
as e-commerce.
In order to strengthen the user authentication
procedure we decided to use a multimodal biometric
identity verification.
2.1 Biometric Verification
Biometrics identity verification can be implemented
by adopting different architectures (Petterson et al.
2002), namely:
Match-on-Card (MoC): verification is
performed by an applet running on the SIM
card. This scheme implies Template-on-Card
(ToC), i.e. the reference biometric templates
must also be stored on the SIM card.
Match-on-Host (MoH): verification is
performed by a trusted application running on
the host (the PDA, in our case). ToC is also
usually implied in this scheme, for privacy
reasons (Bella et al., 2003).
Match-on-Server (MoS ): verification is
performed by an application running on an on-
line Trusted Third Party (TTP) server. In this
scheme, Template-on-Server (ToS) is usually
implied, i.e. the reference biometric templates
must also be stored on the TTP server.
MoC has been adopted as the SecurePhone’s
primary biometric identity verification architecture,
because of the high levels of security and privacy
that it permits to attain - at least on theoretical
grounds. MoH was also implemented as a testbed for
the assessment of MoC verification results.
The MoS model was discarded because it
deviates strongly from SecurePhone original concept
and because of privacy considerations, which
present arguments against the use of central servers
for the storage of sensitive data like biometric
templates. Furthermore, MoS does not seem to
ensure adequate security levels for the purposes of
the SecurePhone project, if not at the cost of
implementing a complex network architecture
exploiting cryptographic technology for securing the
communications between the various entities
2.2. Hardware Requirements
In terms of hardware, the choice has been made to
use a commercial “off-the-shelf” mobile phone
without any particular add-ons. At the moment of
selecting the most suitable platform – early 2004 –
the best choice resulted in the selection of the Qtek
2020 a GSM/GPRS PDA-phone - also known as O2
Xda II, SPV M1000 - manufactured by the
Taiwanese company HTC under the generic
nickname of “Himalaya”. Since GPRS technology
does not enable the simultaneous transmission of
voice and data during a single session, some
limitations descended from this forced decision that
had an influence on service design. Another
drawback, in terms of usability and intrusiveness, is
related to the fact that the Qtek 2020 built-in camera
is on the rear of the device, thus making the capture
of audio-video data more cumbersome. A new
UMTS PDA-phone (the Qtek 9000, a.k.a HTC
Universal) has recently been launched on the market
that will make it possible to overcome these
technical limitations.
Although the SecurePhone is in all respects a
normal PDA-phone, the SIM card that it uses is
special, since it must provide built-in support for
symmetric and asymmetric cryptography and
enough storage space for the needs of MoC
biometric authentication. The SIM card selected for
the project is a GSM-compatible, PKI Java card with
128 KB RAM, providing support for RSA and ECC
2.3 System Architecture
A high-level representation of SecurePhone system
architecture is given in Figure 1.
“P2P” scenario
“P2B” scenario
video camera
Figure 1: system architecture and service models.
All communications between host applications
running on the PDA and applets on the SIM card are
compliant with the Application Protocol Data Unit
(APDU) protocol, defined in ISO-7816 part 4 for
communications with card-based applications.
The functionalities of the specific software
modules required for system operation are briefly
described in the following subsections.
2.3.1 Software Modules on the PDA-phone
Document Exchange Module
This module is a fundamental part of the
SecurePhone user interface. It enables to:
o produce an e-contract - or import it from a
list of predefined document templates;
o transmit the e-contract to another
SecurePhone device over the GPRS
network and receive it back in a possibly
modified form;
o modify a received e-contract interactively
in order to produce a final form the two
users agree on;
o launch the Authentication Module for
biometric authentication against the device
- once an agreement on the contents of the
e-contract has been eventually reached - in
order to verify the identity of the user who
is required to e-sign;
THE “SECUREPHONE” - A Mobile Phone with Biometric Authentication and e-Signature Support for Dealing Secure
Transactions on the Fly
o request the e-Signature Module to e-sign
the e-contract, if the user’s identity has
been verified;
o request the e-Signature Interface Module to
verify the e-signature on an e-contract.
Authentication Module
This module is responsible of:
o acquiring a user’s “live scan” biometric
samples by means of the device sensors
(video camera for face, microphone for
voice and touch screen for handwritten
o pre-processing the acquired biometric
samples in order to produce live scan
biometric parameter vectors;
o sending live scan biometric parameter
vectors to the SIM card for comparison
with enrolment biometric models stored
e-Signature Interface Module
This module interfaces the SIM card for all
tasks related with the creation of e-signatures,
o produce a digest of the e-contract;
o randomly create a symmetric key and use it
to encrypt the e-contract;
o transmit the digest and the symmetric key
in a single bundle to the SIM card in order
to have it e-signed;
o verify the e-signature on an e-contract and
retrieve the symmetric key used to encrypt
o decrypt the e-contract with the retrieved
symmetric key.
2.3.2 Software Modules on the SIM Card
Biometric Verification Applet
This module is implemented as a Java applet
and enables to:
o compare live scan biometric parameter
vectors with enrolment biometric models
that are securely stored onto the SIM card
itself, using a verification threshold for
each individual modality;
o apply a fusion algorithm to the verification
scores obtained by each single biometric
modality, in order to produce a single value
to be verified against a threshold;
o produce the pre-specified “unlocking” code
that is required to enable SIM card
cryptographic services in case of successful
e-Signature Applet
This module is implemented as a Java applet
and is responsible of:
o generating and managing cryptographic
keys on the SIM card;
o controlling the data sent and received with
the e-Signature Interface Module running
on the PDA during a data transfer session;
o recombining data received during a single
o performing the cryptographic operations
involved in electronic signature creation.
SecurePhone’s innovative biometric recogniser
plays an important role in ensuring the overall
dependability of the proposed solution.
The choice has been made from the outset to
exclude biometric identification modalities that may
have social connotations – e.g. fingerprint
recognition. Psychological discomfort is in fact the
first cause of social resistance to biometrics for
identity verification applications. The SecurePhone
solution exploits three biometric modalities –
namely voice, face and handwritten signature
recognition – chosen because of their non-
intrusiveness and friendliness to users as “natural”
identification means. Another important factor that
influenced the choice of these biometrics is that
commercially available PDA-phones are already
equipped with reasonably good sensors to capture
the relevant biometric data, so that no extra
dedicated hardware is required.
The three modalities are fused in a single
biometric recogniser, which has been specifically
designed and developed as a result of extensive
original research. In particular the fusion scheme has
been optimised so as to enhance verification
performance and provide robustness to changing
environmental conditions.
As a further security measure, the biometric
templates used to authenticate a device’s legitimate
owner are stored on the device SIM card during the
enrolment phase and never leave the card during
system operation. Since biometric verification is
performed on card, special care was required to
efficiently adapt biometric algorithms to the
reduced computational and memory resources
provided by currently available SIM cards.
3.1 Data Modelling
Due to their inherent variability, all three of the
biometrics modalities selected require the use of
statistical data models rather than simple templates.
While state of the art models differ between
modalities, we have found that Gaussian mixture
models (GMM) (Duda et al., 2001), used together
with a GMM universal background model (UBM),
give performance which is close to state of the art
for all three modalities. While this is the model of
choice for voice based authentication (Reynolds et
al., 1995), the high performance which this model
also gave for face and signature verification was
unexpected. This is probably because for all three
modalities the amount of enrolment data available
for model training is very restricted. The GMM with
MAP adaptive training (updating the Gaussian
means only) from a UBM is well suited to small
amounts of training data. The UBM serves two
purposes. It is used to initialise the client model
before adaptive training with the enrolment data, and
it is also used as a universal impostor model for
score normalisation (the score used is proportional to
the logarithm of the ratio of the posterior client
probability to the posterior impostor probability). All
three modalities on the PDA use a GMM to model
biometric data features. Models were trained using
the Torch machine learning API (Collobert et al.,
2002). A UBM, pre-trained on data from a number
of speakers, is installed both on the PC where
enrolment takes place, and on the SIM card.
Enrolment then comprises 8 simulated client
accesses, during which time the lighting and
background noise conditions are varied to reflect the
range of conditions expected during use. After
biometric features have been extracted from this
data, these features are used to train a GMM client
model for each modality, which is then installed on
the client’s SIM card (Koreman et al., 2006).
3.2 Face Verification
There are many different face verification schemes.
For efficiency required by mobile devices, wavelet-
based verification schemes were selected for
investigation and development. Wavelet transforms
are multi-resolution image decomposition
techniques that provide a variety of channels,
representing the image features by different
frequency subbands at different scales. Various
combinations of wavelet filters, frequency subbands,
and levels of decomposition were developed for
implementation on the adopted PDA. Several
lighting normalisation procedures were also
investigated, since they can substantially improve
face recognition under the variable conditions in
which the SecurePhone is used. The performances of
some of these schemes were extensively tested on a
number of benchmark biometric databases as well as
on a newly created audio-visual database (the
The PDAtabase was primarily designed to test
fixed-prompt based user authentication on the
QTEK 2020, using biometrics from voice, face and
handwritten signature. Video data was recorded,
using the PDA-phone, from sixty English speaking
subjects (80% native) at 44 kHz audio and 20 frames
per second video. Each subject was recoded in two
well separated sessions. Each session was recorded
under two different inside lighting and noise
conditions and two different outside conditions. Six
examples of each of three different prompt types
were recorded under each condition (5 digits, 10
digits and short phrases). Signature data was
recorded from sixty separate subjects. Each subject
recorded twenty repetitions, and was impostorised
twenty times (by one other person).
For more details on the face biometric, testing
experiments and the PDAtabase we refer the reader
to (Morris et al., 2006) (Sellahewa et al., 2005)
(Sellahewa et al., 2006).
3.3 Speaker Verification
Voice features use 19 Mel-frequency cepstral
coefficients (MFCC, without c0), with cepstral mean
subtraction (CMS) to remove convolutive noise, and
non-speech removal to reduce uninformative data.
First order time difference features are then added
(Reynolds et al., 1995). All processing is online, so
that feature processing can start before the utterance
has been completed. While the PDA is capable of
sampling at 44 KHz, sampling was set to 22 KHz as
this reduces processing time without compromising
verification accuracy.
3.4 Handwritten Signature
Signature data is captured from the PDA touch
screen at 100 (x,y) samples per second. This
sequence of 2 dimensional data is then processed to
give a sequence of 19 dimensional feature vectors
(Dolfing, 1998). The glass touch screen is not an
ideal surface for writing on. PDAtabase tests
(signatures of 64 different writers acquired by using
the Qtek 2020) showed that signatures obtained in
this way could give good verification accuracy, but
not as good as signatures obtained from a dedicated
writing tablet which also measures pen pressure and
two pen angles (Garcia-Salicetti et al., 2003).
THE “SECUREPHONE” - A Mobile Phone with Biometric Authentication and e-Signature Support for Dealing Secure
Transactions on the Fly
3.5 Fusion
Each of the three biometric modalities can be used
separately to perform the identity verification, but
the combination of the three systems has several
advantages. Firstly, multimodality is expected to
strongly enhance person authentication performance
in real applications as shown in (Allano et al., 2006).
Secondly, operational conditions generate
degradations of input signals due to the variety of
environments encountered (ambient noise, lighting
variations, …), while the low quality of sensors
further contributes to decrease system performance.
By fusing three different biometric traits, the effect
of signal degradation can be counteracted.
In order to combine several biometric modalities,
fusion can be performed at different levels: feature
level, score level or decision level. Many fusion
techniques have so far been compared in the
literature. In (Allano et al., 2006), two types of score
fusion methods have been compared on the
PDAtabase (Morris, Koreman, Sellahewa, Ehlers,
Jassim, Allano, Garcia-Salicetti, 2006) (Morris,
Jassim, Sellahewa, Allano, Ehlers, Wu, Koreman,
Garcia-Salicetti, Ly-Van, Dorizzi, 2006). The first
type is based on the Arithmetic Mean Rule after a
previous normalization of each score separately. The
second type is based on modelling the 3D
distribution of client and impostor scores, for
example using a Gaussian Mixture Models (GMM).
After testing a number of different fusion methods
suited to the limited computing capability of the
PDA, the method selected for implementation was
GMM based fusion (Allano et al., 2006) (Koreman
et al., 2006). In this model, during enrolment two
scores GMMs are installed in the PDA. One is
trained to model the joint distribution of client
scores and one the joint distribution of impostor
scores. These scores GMMs were first trained on a
large amount of scores data by combining data from
all six of the 5-digit prompts tested, and then
retrained on data from the single prompt selected for
use in the working PDA, updating the Gaussian
means only. During verification the client match
scores from each modality are concatenated into a
single vector and from this the client-scores GMM
estimates a client log likelihood and the impostor-
scores GMM estimates an impostor log likelihood.
The difference of these log likelihoods provides a
log likelihood ratio, which is the combined score
against which the accept/reject decision is made
using a suitably estimated threshold.
3.6 Forgery Scenarios
As with any security system, the level of security
depends on the effort which an impostor is prepared
to invest. In the case of the present fixed prompt
system with static face recognition, if a photograph
of the owner’s face and signature together with a
high quality recording of their reading the fixed
prompt was obtained, then successful
impostorisation would be possible. This imposture
scenario could be avoided if it were feasible to
implement the liveness test proposed in (Bredin et
al., 2006), in which a check is made on the degree of
correlation between mouth opening and speech
energy. However, the present PDA is not capable of
the computation required for mouth tracking. Such
issues may require the development of suitable
dedicated hardware (Koreman et al., 2006).
Table 1: EER, FAR and FRR % scores (for 3 typical values of the false acceptance to false rejection cost ratio, R) obtained
with the PDAtabase. Scores were obtained using a threshold optimised for data from one set of speakers while testing on
another set. For test details, see (Morris, Koreman et al., 2006).
R=0.0 R=1.0 R=10.0 R=0.0 R=1.0 R=10.0
6.12 19.10 4.81 0.86 2.08 8.33 19.10
28.57 93.77 26.44 1.18 1.16 30.44 85.53
6.19 13.61 6.94 4.31 2.78 4.86 52.78
All 3
0.85 2.15 1.90 0.39 0.81 1.16 3.94
Although the SecurePhone project has not been
finished yet, a first prototype of the system has been
implemented and is under evaluation at the moment
of writing. The prototype includes the module for
document exchange as well as a first release of the
authentication module (biometric recogniser), which
is presently running on host (MoH biometric
verification). The MoC verification applet is in
advanced development phase, while the e-signature
applet has been fully implemented and is currently
under test in a simulated environment, before final
deployment on the SIM card.
Prior to implementation on the PDA, the
performances of the biometric recogniser were
thoroughly investigated on a desktop workstation in
an environment that closely emulates the operational
conditions expected on the mobile device. Table 1
shows test results obtained from a database which
was recorded on the PDA (Morris et al., 2006).
Results are averaged over separate tests for six
different 5-digit prompts. The prompt with the best
score (“28376”) was used in the PDA. 10-digit
prompts lower the fused average EER from 0.85% to
0.56%, but 5-digit prompts reduce preprocessing
time. Further reduction in error rate could be
obtained if more memory was available for
biometrics model storage. Voice, signature and face
models presently require 23.0, 2.9 and 11.6 Kb
respectively. Tests run directly on the PDA are in
progress at the moment of writing..
Figure 2: A screenshot of the SecurePhone system
Figure 3: SecurePhone system prototype.
The very promising results obtained so far in the
SecurePhone project encourage us to investigate
their possible exploitation in various directions. The
primary effort will be to further improve the
performances of the biometric recogniser and
implement other operation modes. Present
restrictions in terms of user interface and overall
usability will be overcome in the immediate future
by the adoption of the recent Qtek 9000, running
Windows Mobile 5.0, with integrated UMTS
support and a CIF camera in the front.
Another line of development that is presently under
investigation is focused on exploiting the
SecurePhone biometric technology to realise a
“seamless recogniser”. The idea is to use combined
face and speaker recognition in the initial phase of a
video call for the mutual identification of the two
parties involved in the video call itself, who do not
need to know each other personally. A success in
mutual identification could seamlessly trigger the
encryption of the communications between the two
parties. Such a system can find countless
applications in all sectors where high levels of trust
and confidentiality are required – intelligence, the
military, safe communication of trade secrets, etc.
A further, more visionary step in the development of
SecurePhone outcomes extends the concept of
biometric multimodal identification beyond the
scope of mobile communications, by realising a
multiplatform biometric recogniser suitable to be
used in general network applications. This idea is
closely related to current research on identity
management for universal access, an emerging field
in information and communications technology.
THE “SECUREPHONE” - A Mobile Phone with Biometric Authentication and e-Signature Support for Dealing Secure
Transactions on the Fly
The vision embodied in the SecurePhone project is
to reduce the psychological intimidation often felt by
ordinary users towards new ICT technologies by
proposing new advanced uses for a familiar and
intuitive communication platform such as the mobile
phone. Although supplemented with high-tech
functionalities, the SecurePhone does not differ from
a common PDA-phone in terms of ease of use and
user-friendliness. Under its surface appearance,
though, a remarkable level of innovativeness is
hidden: by means of the SecurePhone users will be
given the opportunity to draw legally valid e-
transactions, relying on the security provided by
electronic signature for a whole new set of possible
social interactions and business opportunities.
This work was supported by the EC SecurePhone
project IST-2002-506883
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