BIOMETRIC AUTHENTICATION DEVICES AND SEMANTIC

WEB SERVICES

An Approach for Multi Modal Fusion Framework

L. Puente Rodríguez

Universidad Carlos III de Madrid, Avda de la Universidad, 30, Leganés, Madrid, Spain

M. J. Poza

Universidad Francisco de Vitoria. Ctra. Pozuelo-Majadahonda Km. 1.800. Pozuelo de Alarcón, Madrid, Spain

J. M. Gómez, B. Ruiz

Universidad Carlos III de Madrid, Avda de la Universidad, 30, Leganés, Madrid, Spain

Keywords: Biometrics, Authentication Devices, Emerging Technologies, Data Integration, Semantic Web Services,

Ontologies.

Abstract: Identity verification is now a days a crucial task for security applications. In the near future organizations

dedicated to store individual biometric information will emerge in order to determine individual identity.

Biometric authentication is currently information intensive. The volume and diversity of new data sources

challenge current database technologies. Biometric identity heterogeneity arises when different data sources

interoperate. New promising application fields such as the Semantic Web and Semantic Web Services can

leverage the potential of biometric identity, even though heterogeneity continues rising. Semantic Web

Services provide a platform to integrate the lattice of biometric identity data widely distributed both across

the Internet and within individual organizations. In this paper, we present a framework for solving biometric

identity heterogeneity based on Semantic Web Services. We use a multimodal fusion recognition scenario

as a test-bed for evaluation.

1 INTRODUCTION

Identity recognition is performed now a days by the

use of traditional techniques such as PINs,

passwords, digital signatures, etc. Biometrics

promise to offer a new alternative, portable, easy to

use, free of memory, loss or theft problems. A global

solution will be based on the creation of specialized

organizations offering authentication services. This

Biometric Accreditation Entities (BAE) will base

their services on previously acquired biometric data.

Biometrics authentication usually refers to the

identification of an individual based on his or her

distinguishing traits. In principle, a biometric

identity is based on the premise that a measurable

physical or behavioural trait is a more reliable

indicator of identity than the traditional systems such

as pairs composed by password and username,

Personal identification numbers (PIN) and the akin.

Particularly, since biometric identity technologies

deal with security and privacy issues, the challenge

for the research community is to attain integrated

solutions that address the entire problems from

sensors and data acquisition to biometric data

analysis and system design.

Presently, the lack of performance of biometric

systems is being alleviated by the use of multiple

biometric indicators for identifying an individual in

order to increase its accuracy when using a

technique called Multimodal Fusion (Kittler, J.

Hatef, R. Matas, J. G., 1998) (Jain R. and J. Quian,

2001). As a result of this, biometric information has

grown exponentially and algorithms for feature

extraction, matching score or decision levels handle

a tremendous amount of data. Furthermore, the

Puente Rodríguez L., J. Poza M., M. Gómez J. and Ruiz B. (2008).

BIOMETRIC AUTHENTICATION DEVICES AND SEMANTIC WEB SERVICES - An Approach for Multi Modal Fusion Framework.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 95-100

DOI: 10.5220/0001046300950100

 SciTePress

recent years have provided with a lattice of

duplicated efforts in building test databases such as

face recognition databases (e.g. FERET, PIE or

BANCA) (Bailly-Baillière E., S. Bengio et al., 2003)

and a lack of uniform standards and granted open

access to these databases, as discussed in (Ming, A.

Ma, H., 2007).

Hence, arguably the most critical need in

biometric identity recognition is to overcome

semantic heterogeneity i.e. to identify elements in

the different databases that represent the same or

related biometric identities and to resolve the

differences in database structures or schemas, among

the related elements. Such data integration is

technically difficult for several reasons. First, the

technologies on which different databases are based

may differ and do not interoperate smoothly.

Standards for cross-database communication allow

the databases (and their users) to exchange

information. Secondly, the precise naming

conventions for many scientific concepts in fast

developing fields such as biometrics are often

inconsistent, and so mappings are required between

different vocabularies.

Hence, we present in this paper a framework for

solving multimodal fusion oriented biometric

identity data heterogeneity problems, keeping the

structure of databases created with the aim of being

used for identity accreditation and distributed over

the Web. Our approach is based on the breakthrough

of adding semantics to Web Services which perform

a role of entry points for such databases.

Fundamentally, this implies that our framework

enables different biometric identity data to be

discovered, located and accessed since they provide

formal means of leveraging different vocabularies

and terminologies and foster mediation.

The remainder of this paper is organized as

follows. In Section 2, a brief state-of-the-art on the

technologies employed in our research is given.

Section 3 defines some terms we use along this

paper. Section 4 identifies the heterogeneity of data

involved in the biometric identification process.

Section 5 describes the framework for solving

problems using Semantic Web Services. Finally,

conclusions and related work are discussed in

Section 6.

2 STATE-OF-THE-ART

Semantic Web Services and Ontologies are the

cornerstone technologies applied in our research. On

the one hand, data interoperability between different

information sources is achieved by means of

ontologies and their mapping. On the other hand,

Web Services semantically annotated are the

software entities responsible for providing a

normalized interface to disparate functionality and

data sources. In this section, a brief description of

each of these technologies is put forward.

2.1 Ontologies

Although a number of different ontology definitions

can be found currently in literature, in this work we

use Borst’s one (Borst, W.N., 1997): “an ontology is

a formal specification of a shared

conceptualization”, where ‘formal’ refers to the need

of machine-understandable ontologies. This

definition emphasizes the need of agreement in

carrying out a conceptualization. On the other hand,

‘shared’ refers to the type of knowledge contained in

the ontologies, that is, consensual, non-private

knowledge. In this work, this definition of ontology

has been adopted.

Ontologies have become the de-facto standard

knowledge representation technology after the

emergence of the Semantic Web along with

Semantic Web Services and the Semantic Grid. For

all these new research branches, ontologies are the

cornerstone technology. Knowledge in ontologies is

mainly formalized using five kinds of components:

classes, relations, functions, axioms and instances

(Gruber, T. R., 1993). There are several formal

languages used to construct ontologies, that is,

ontology languages, including KIF, OCML and F-

Logic. Along with the Semantic Web, new markup

ontology languages have come out such as SHOE,

DAML+OIL, and the current de facto standard,

OWL (Web Ontology Working Group, 2004).

2.2 Semantic Web Services

Semantic Web Services are a new technology

resulting from the combination of other two

technologies, namely, the Semantic Web and Web

Services. On the one hand, the Semantic Web (SW)

aims at adding semantics to the data published on

the Web (i.e., establish the meaning of the data), so

that machines are able to process these data in a

similar way a human can do (Berners-Lee, T.,

Hendler, J., Lassila, O., 2001). Ontologies are the

backbone technology of the SW as they provide

structured vocabularies that describe the

relationships between different terms, allowing

computers (and humans) to interpret their meaning

flexibly yet unambiguously.

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

On the other hand, Web Services (WS)

technology extends the Web from a distributed

source of information to a distributed source of

functionality. It is based on a set of standard

protocols, namely, UDDI (Universal Description,

Discovery and Integration), SOAP (Simple Object

Access Protocol), and WSDL (Web Services

Description Language). Therefore, WS provide the

means to develop globally accessible loosely-

coupled applications. However, as the Web grows in

size and diversity, there is, composition and

invocation can be carried out by autonomous

software entitiesan increased need to automate traits

of WS such as discovery, selection, composition and

execution. The problem is that current technology

around UDDI, WSDL, and SOAP provide limited

support for all that (Fensel, D. & Bussler, C., 2002).

As a consequence, the principles behind SW

technology were applied to WS leading to what we

know as Semantic Web Services (SWS) technology.

It consists of annotating WS with semantic content

so that service discovery.

The W3C is currently examining various

approaches with the purpose of reaching a standard

for the SWS technology: OWL-S (OWL Web

Ontology Language for Services) (OWL-S W3C

Submission, 2004), WSMO (Web Service Modeling

Ontology) (WSMO W3C Submission, 2005), SWSF

(Semantic Web Services Framework) (SWSF W3C

Submission, 2005), and WSDL-S (Web Service

Semantics) (WSDL-S W3C Submission, 2005). The

two most widespread approaches are OWL-S and

WSMO. OWL-S is an ontology for services that

makes it possible for agents to discover, compose,

invoke, and monitor services with a high degree of

automation. Similarly, WSMO provides a

conceptual framework for semantically describing

all relevant traits of WS in order to facilitate the

automation of discovering, combining and invoking

electronic services over the Web.

3 SOME DEFINITIONS

A trait is defined as any physical, motor or

psychomotor human characteristic capable of being

used in biometric identification.

A user is any person for the system to recognize,

and whose traits are stored somehow in the database.

A donor is every person (user or not) whose trait

is captured, voluntary or involuntary, by a sensor of

the system.

A sample is defined in (Mansfield, J. Wayman, J.L.,

2002) as a biometric measure presented by the donor

which eventually results in an image or signal.

4 IDENTITY HETEROGENEITY

PROBLEMS

A typical biometric system presents a well defined

structure (Mansfield, J. Wayman, J.L., 2002) that

includes two phases: enrolment and testing.

Enrolment faces the creation of a type of model

representing the user in a univocal way, while

testing tries to determine if the donor matches or not

the model.

This two phases share the steps related to sample

acquisition, pre–processing signal and feature

extraction. Enrolment completes its chain with a

model creation, while testing do it with a matching

step.

Figure 1: The process.

Acquisition implies that one or more sensors acquire

one or more samples of certain donor biometric

traits presented to the biometric systems (e.g.

fingerprint, face, iris image). Different kind of

sensors capturing different biometric donor traits

generate different kind of samples.

After capture, samples are pre–processed by

cleaning and normalizing in order to adapt the signal

to further data extraction, which means signal

BIOMETRIC AUTHENTICATION DEVICES AND SEMANTIC WEB SERVICES - An Approach for Multi Modal

Fusion Framework

filtering, enhancement, energy detection, image

centring...

The feature extraction module obtains certain

information values supposedly related to the donor

in a univocal way. These values are collected in sets

called “feature vectors”. Different feature extraction

algorithms generate different vectors types.

During enrolment a set of homogeneous vectors

are used to create a new model of the related trait of

the user. While testing the identity claim user model

is compared with another set of vectors obtained

from the current donor.

For mono–modal biometric systems only one

trait is scanned, and of course only one model is

generated for each user.

In our multi–modal biometric fusion approach,

multiple user models are generated: one for each

modality. Every kind of sensor, scanning different

traits, generates different signals. Every one of them

flows through different acquisition–preprocessing–

extraction chains. The testing phase matches the

resulting vectors of every chain with the

correspondent model, obtaining as a result a

confidence level which informs of the probability

that the sample belongs to the user identity claim.

Finally, these results are collected by a decision

module which will decide to validate or reject the

donor.

Different capture, pre-processors or feature

extractor algorithms generate different kind of

models. Then, other kind capture–process–extractor

chain generate feature vectors that should not match

properly the model. That’s why semantic

information should be added to raw data in order to

identify such a variety.

5 THE FRAMEWORK

All over the world we can find heterogeneous

databases as a set of biometric recorded data. In this

section, we present an integrated approach that

address the entire problem of enabling entities

(organizations) to confirm individual authentication,

based on biometric models (traits) stored in different

databases all over the Web. The main requirements

of our framework are as follows:

• Provide a platform that allows data matching of

acquired biometric samples against individual

biometric models (traits) stored in certain databases

• Provide catalogues of data that allows to

determine a given model location and make these

catalogues available via the Web.

• Use of the Web Service technology to make

this access a reality and provide the plumbing

communication technology over the wire.

• Use of Semantics to find the most accurate

model source for the biometric testing that is taking

place.

• And finally use available Web Services in order

to ask for autentication for acquired samples.

In our framework, we have addressed this

process, taking into account the growing complexity

of having a multimodal biometrics test. For that, we

notice that our work is mostly oriented to multiple

biometric fusion strategies, where multiple biometric

measures are utilized (Kittler, J. Hatef, R. Matas, J.

G., 1998).

A graphical representation of our framework is

depicted in the figure 2.

Figure 2: The framework.

The first part of the aforementioned steps i.e. the

capture of the biometric samples and certain steps of

the signal-process is addressed by the Biometric

Data Acquisition System. The goal of this system is

to encapsulate the biometric measure with a “data

cover” that includes the type of the measure (voice,

image, fingerprint, etc.) and a number of significant

attributes that can describe the measure and the

identity claim.

The second one, the Semantic Service Engine,

takes the measure and its “data cover” and accesses

the different data storages looking for the best fitting

for the cover along the Web. A Semantic Service is

an execution environment for the Semantic Web

Services initiatives described in section 2. Along

with some of the W3C submissions, different tools

have been implemented that bring together the major

Semantic Web Services functionalities in an

integrated framework. One example is WSMX (Web

Services Execution Environment), an execution

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

environment to perform dynamic discovery,

selection, mediation, invocation and inter-operation

of Semantic Web Services. Another example is IRS

(the Internet Reasoning Service), a Semantic Web

Services framework which allows applications to

semantically describe and execute Web Services.

The IRS system supports the provision of semantic

reasoning services within the context of the

Semantic Web.

In both cases, a Semantic Service Engine would

take the measure and its “data cover” to hook it up

with the Web Service that fits the most and behaves

as an entry point of the aforementioned databases, as

it is show in Figure 1.

Finally, the Decision Module component

produces a typical index called matching score. A

match or no-match decision can be made according

to whether this score exceeds a decision threshold or

not. This implies an attempt to validate or not the

claim of identity, which outcomes a final decision

about the biometric identity.

6 CONCLUSIONS

AND RELATED WORK

Since Biometric technologies are intended to tackle

security and privacy issues, the integration of access

control mechanisms and information security are

also areas of growing interest.

The creation of Biometric Accreditation Entities

will be an alternative in the near future to the current

digital certification organisms.

In this environment the heterogeneity of sample

capture and data process should not become a barrier

for the use of this identification technology.

As the use of Semantic Web Services grows, the

problem for searching, interacting and integrating

relevant services is becoming increasingly a hurdle

for the leverage of existing Semantic Web

technologies which have reached a certain level of

maturity.

In this paper, we have proposed a conceptual

approach for a effective solution in this

heterogeneous environment. It is based on the

application of the Semantic Web Services properties.

It requires to add semantic to the data stored in the

biometric accreditation entities databases, and

provide the adequate services to the SWS servers.

It has already been proposed the idea of using

agents. They can take advantage of the machine-

processable metadata provided by the Semantic Web

Services. In (Hendler, J., 2001), the author points out

how the ontology languages of the Semantic Web

can lead to more powerful agent-based approaches

for using services offered on the Web. A more

practical approach is shown in (Gandon, F. and

Sadeh, N., 2004), where the authors describe an

application where intelligent agents, aided by

context information provided by Semantic Web

Services, assist their users with different sets of

tasks.

Finally, our future work will focus on creating a

complete adapted ontology and to define a standard

for required services on SWS, identifying real–world

scenarios and validating the efficiency of our

approach and to determine its feasibility. This work

is related to existing efforts about ontology merging

and alignment. A future version of our framework

will be orientated towards that direction.

ACKNOWLEDGEMENTS

This work is founded by the Ministry of Science and

Technology of Spain under the PIBES project of the

Spanish Committee of Education & Science

(TEC2006-12365-C02-01).

REFERENCES

Bailly-Baillière E., S. Bengio et al., 2003. “The BANCA

Database and Evaluation Protocol,” in Springer

LNCS-2688, 4th Int. Conf. Audio- and Video-Based

Biometric Person Authentication, AVBPA’03. 2003,

Springer-Verlag.

Berners-Lee, T., Hendler, J., Lassila, O., 2001. The

Semantic Web. Scientific American, May 2001, pp.

34-43.

Borst, W.N., 1997. Construction of Engineering

Ontologies for Knowledge Sharing and Reuse. PhD

Thesis. University of Twente. Enschede, The

Netherlands.

Fensel, D. & Bussler, C., 2002. The Web Service

Modeling Framework WSMF. Electronic Commerce

Research and Applications, 1(2).

Gruber, T. R., 1993. A translation approach to portable

ontology specifications. Knowledge Acquisition Vol.

5:199-220.

Web Ontology Working Group, 2004. OWL Web

Ontology Language Guide.

OWL-S W3C Submission, 2004. OWL Web Ontology

Language for Services. Available at:

http://www.w3.org/Submission/2004/07/

WSMO W3C Submission, 2005. Web Service Modeling

Ontology. Available at: http://www.w3.org/

Submission/2005/06/

BIOMETRIC AUTHENTICATION DEVICES AND SEMANTIC WEB SERVICES - An Approach for Multi Modal

Fusion Framework

SWSF W3C Submission, 2005. Semantic Web Service

Framework. Available at: http://www.w3.org/

Submission/2005/07/

WSDL-S W3C Submission, 2005. Web Service

Semantics. Available at: http://www.w3.org/

Submission/2005/10/

Hendler, J., 2001. Agents and the Semantic Web. IEEE

Intelligent Systems, 16(2): 30-37, March/April 2001.

Gómez, J. M., Rico-Almodóvar, M., García-Sánchez, F.,

Martínez-Bejar, R. & Bussler, C., 2004. GODO: Goal-

driven Orchestration for Semantic Web Services.

WSMO Implementation Workshop, September 2004.

Jain, K. Bolle, R. et al. Biometrics, 1999: Personal

Identification in Networked Society. Kulwer

Academic. 1999.

Jain R. and J. Quian, 2001: Information Fusion in

Biometrics. Proc. 3rd International Conference on

Audio and Video Based Person Authentication

(AVBPA) pp. 354-391,Sweden, 2001.

Gibbins, N., Harris, S., Shadbolt, N., 2003. Agent-based

Semantic Web Services. In Proc. of the 12

Int. World

Wide Web Conference, May 2003.

Gandon, F. and Sadeh, N., 2004. Semantic Web

Technologies to Reconcile Privacy and Context

Awareness. Web Semantics Journal, 1(3), 2004.

Ming, A. Ma, H., 2007. An Algorithm Tested for the

Biometrics Grid. Proceedings of the Second

International Conference in Grid and Pervasive

Computing (GPC07). Paris, France. 2007.

Mansfield, J. Wayman, J.L., 2002. Best Practices in

Testing and Reporting Performance of Biometric

Devices. National Physics Lab for Mathematics and

Scientific Computing. 2002.

Kittler, J. Hatef, R. Matas, J. G., 1998. On Combining

Classifiers. IEEE Transactions on PAMI, vol. 12

(1998). Pp. 226-339.

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

100