Christophe Gravier

Jacques Fayolle

Jeremy Lardon

Martin J O'connor

Jérémy Lardon

Adaptive System for Collaborative Online Laboratories

Keywords: Ontology-driven Applications, Adaptive Systems, Remote Laboratories, Computer Supported Collaborative Learning, Web Based Instruction, Ontologies, OWL, SWRL, Rule-Based Systems, Knowledge-Based Systems

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

T HE field of Online Engineering emerged in the early 2000s from research carried out at the Massachusetts Institute of Technology [START_REF] Wasserstein | Students in Africa get web link to MIT labs[END_REF]. Using the Internet and webbased technologies, the goal of online learning is to provide a laboratory learning experience to students who are not physically present in a laboratory. These online laboratories allow students in the various parts of the world to perform engineering laboratories online and use Distance Learning techniques to produce the pedagogical equivalent of in situ laboratory sessions [START_REF] Wasserstein | Students in Africa get web link to MIT labs[END_REF], [START_REF] Aktan | Distance Learning Applied to Control Engineering Laboratories[END_REF], [START_REF] Rosenblatt | Designing and Implementing Hands-On Robotics Labs[END_REF], [START_REF] Lang | Pedagogical evaluation of remote laboratories in eMerge project[END_REF], [START_REF] Agrawal | WebLab: A Generic Architecture for Remote Laboratories[END_REF]. This field grew out of early work that aimed for accurate online reproduction of workbench activities during local laboratory sessions [START_REF] Aktan | Distance Learning Applied to Control Engineering Laboratories[END_REF], [START_REF] Gillet | eMersion: a New Paradigm for Web-based in Engineering Education[END_REF], [START_REF] Schäfer | PEARL: A generic architecture for live experiments in a remote laboratory[END_REF].

Current Online Laboratories differ from traditional laboratories in that they typically lack the ability to support collaboration among students [START_REF] Dennis | Satellite Control Using Rational Agent Programming[END_REF]. When developing current online systems, researchers are primarily interested in the mechanics of making a laboratory available online, and in the integration of the laboratory with local information systems, especially Learning Management Systems. While these issues are important, the inability to support multiple simultaneous users results in systems that lack a key component of the traditional laboratory learning experience. Collaboration among students is a cornerstone of the local laboratories learning experience as it lets students exchange skills, results, and knowledge, to form groups, and to emulate other group members [START_REF] Suebnukarn | COMET: A Collaborative Tutoring System for Medical Problem-Based Learning[END_REF]. Recent research has focused on how to scenarized these interactions in a learners group [START_REF] Tsovaltzi | Extending a virtual chemistry laboratory with a collaboration script to promote conceptual learning[END_REF], [START_REF] Mclaren | Learning Chemistry through Collaboration: A Wizard-of-Oz Study of Adaptive Collaboration Support[END_REF]. Co-construction of knowledge is also one of the basic goals of collaborative learning. These various dimensions of collaboration are important in Engineering Education, particularly in laboratory settings, as they provide the perspective of shaping teaching scenarios that are close to real-world distributed engineering team work [START_REF] Müller | Collaborative Remote Laboratories in Engineering Education: Challenges and Visions[END_REF]. By learning together, students also learn to work in a distributed group of workers, which is otherwise difficult to learn during lectures. Working at distance is also likely to be an important facet of their future life as engineers [START_REF] Tate | I-Room: A Virtual Space for Intelligent Interaction[END_REF].

To address this limitation, we have developed a collaborative online learning framework with integrated group awareness support [START_REF] Gravier | Putting Reusability first: a paradigm switch in Remote Laboratories Engineering[END_REF]. In this system, students connected to an online session are notified not only of the effects of their intended action, but also notified of the possible interactions between this action and the actions of other users. Each online student is assigned a unique visual indicator (usually a color) for the duration of a session and this indicator is used to show the author of each action. When a student indicates that they intend to use a widget, the widget is modified to display the visual indicator assigned to the student. Every connected user is thus aware of the author of every action, and of its consequence. A scenario of this type is illustrated at figure 1, and in an online demonstration video 1 .

In such a system, multiple simultaneous users attempting to manipulate the same resource can lead to widget wars, which can negatively affect collaboration among learners. In general, a supervisor is present to monitor intendend actions and mediate as needed. To provide assistance to the tutor in the management of the group of connected students, we have developed an ontology-based intelligent system to encode and execute collaboration policies. These policies can be used to adapt the rule governing collaboration among students according to the context of the learning environment and the pedagogical goals of the tutor. Real laboratory (top) and associated software (bottom). The equipment is a Vector Network Analyzer. It is used to measure parameters of an electronical network to determine its signal transmission and reflection capacities.

Using semantic technologies to assist education activities is a relatively recent development [START_REF] Tiropanis | Semantic Technologies for Learning and Teaching in the Web 2.0 Era[END_REF]. Our approach makes use of these technologies to manage collaboration among learners. We provide a domain ontology for collaborative online learning, and present a rule-based system for a tutor to express their strategies in managing these collaborations sessions. We describe a set of example policies and present the rules used to encode those policies. We outline the architecture and an implementation of this semantic system, and describe a user evaluation. This paper is organized as follows. Section 2 outlines the requirements for the modeling of a collaborative online laboratory. Section 3 and describes the OWL ontology used to model the domain of collaborative online learning. Section 4 shows how it is possible to build strategies for collaboration using this ontology by providing examples of common collaborative policies used in online laboratories. The architecture and implementation of the software system developed to realize these strategies are described in Section 5. Finally, we describe an evaluation showing how teachers and students reacted to this new collaborative learning experience.

II. REQUIREMENTS OF A COLLABORATIVE ONLINE LABORATORY

A. Use Case Scenario

Bob and Alice have participated in a large number of laboratories together during the first two of their 3-year Masters curriculum at the University of Saint-Etienne in France. In their final year, Alice and Bob have chosen a distance learning curriculum which allows them to take 9-month rather than the traditional 6-month internships. Alice is working in a company in London, and Bob, who is fond of Japanese culture, has an internship position in the University of Tokyo. Once a week, they perform distance learning sessions. During these sessions, among other learning activities, they participate in a collaborative online laboratory. They participate in this laboratory under the guidance of Mr. Smith, who is connected at the same time. This laboratory sessions concern signal reflection and transmission in telecommunications and requires the use of a network vector analyzer. In addition to the subject-specific goal of the laboratory activity, Mr. Smith views the teaching of collaboration between Bob and Alice as an important part of the learning experience.

B. User interactions in Collaborative Online Laboratories

Anticipating the sequence of interactions between users during a session is difficult. The goals of a particular session and thus the interaction sequences may vary depending on the pedagogical goals of the instructor. The goals may also vary due to the individual circumstances of a session. For example, Mr. Smith may decide that Bob, who arrived late to an online session, should have more time than Alice during the session to allow him to catch up with Alice, who arrived at the beginning of the session. Or instead the tutor may want to favor Alice, who was on time. Alternatively, the tutor may decide to give more time to Alice, because she is less confident than Bob about the topic being covered in the session.

Whatever the scenario, the collaborative policies adopted for a session can vary considerably. The range of possible policies is numerous and each policy can potentially be modified to target the goals of an individual session. For these reasons, it is generally not practical to build a moderation unit that can anticipate all possible collaborative policies in advance. Instead, developing a library of policies that covers a range of possible scenarios offers a more practical solution.

Using Semantic Web technologies, we have developed such a policy library. Using the Ontology Web Language (OWL [START_REF] Smith | OWL Web Ontology Language Guide[END_REF]) and the associated Semantic Web Rule Language (SWRL2 ) we have developed a knowledge-based system to describe and operationalize policies in a collaborative online laboratory. The system uses OWL to describe a domain ontology containing the core entities a typical collaborative online environment. It then uses SWRL to encode individual collaborative policies in terms of these domain entities. An individual policy thus corresponds to a set of SWRL rules; a particular policy may be selected by activating the rule set describing that policy.

III. A DOMAIN ONTOLOGY FOR COLLABORATIVE ONLINE LABORATORIES

We devloped a domain ontology to describe all core concepts and entities that occur in a collaborative online laboratory. Fig. 2 contains a simplified representation of this ontology. The ontology focuses on three basic areas:

1) The role of the user in an online session. Session participants include learners, teachers and administrators. Policies are generally focused on learners, with the ability to formulate policies typically being restricted to teachers and administrators. The ontology describes these various types of session participants. Each of these participant types are associated with different capabilities.

2) The experience level of the user. Various levels of experiences can be assigned to a user, such as, for example, beginner, intermediate, advanced, and expert. For instance, if Alice is familiar with the device she may be assigned an intermediate role, while Bob, who is not, is assigned the role of beginner. These experience levels can be used to constrain the activities a user can perform in a session and to formulate policies based on a user's experience level. 3) The role of time-based elements. Time is a central dimension in online laboratories. Policies are generally expressed in terms of the duration, timing, and sequence of events in an online session. The ontology describes the various temporal properties of the entities involved in a session, such as, the connection time of a user, the amount of time a user has waited or has been an operator, and so on. The ontology was developed on OWL 3 . Simplicity was a major goal when designing this ontology since this approach generally favours reusability, evolution, and sustainability of the ontology [START_REF] Rousset | Small Can Be Beautiful in the Semantic Web[END_REF]. An additional advantage is that it is easier to author and maintain policy rules against a simple ontology.

IV. RULE-BASED POLICIES FOR COLLABORATIVE LEARNING

A. Basic collaborative policies

We can now use this ontology to develop collaborative learning policies. For example, we can describe policies to encode the collaborative learning experiences that the aforementioned Mr. Smith wants to provide to both Bob and Alice. Each policy will, for example, encode access conditions for the operator in a session. Individual policies must also anticipate that collaborative sessions evolve over time and consider the state or collaborative context at a particular time in a session. A policy effectively encodes permissible actions within each collaborative context. In other words, users are able to perform commands in a group only if the collaborative context satisfies a given policy.

In our system, we described these collaborative policies using sets of declarative rules encoded using SWRL 4 . SWRL has a number of attractive properties that make it particularly suitable for this task. It is a declarative language based on OWL and it semantics are built on the same description logic foundation that underlies OWL. It allows users to write rules directly in terms of concepts in an OWL ontology. SWRL rules are stored in the associated OWL ontology and effectively form part of it.

For example, let us build an example collaborative policy rule, which can be expressed in natural language as: Policy 1 : If a user has an administrative role and a pending request for being an operator, then make this user the new operator.

Under this collaborative policy, when Mr. Smith requests to be the operator he becomes the operator even if other users are connected to the system. The next step in building a collaborative policy is to take the natural language description of an individual policy and then express it as a SWRL rule. This rule can be written in SWRL as follows:

        
Def hasOperator : RemoteLab(?r) ∧ U ser(?x) ∧ hasRole(?x, Administrate) ∧ hasP endingOperatingRequest(?x, ?r) ⇒ hasN extOperator(?r, ?x)

(1) This rule can also be expressed as two rules, with one rule for user qualification, and one rule for asserted the next operator.

                  
Def isAdmin : U ser(?x) ∧ hasRole(?x, Administrate) ⇒ Admin(?x) Def hasOperator : RemoteLab(?r) ∧ Admin(?x) ∧ hasP endingOperatingRequest(?x, ?r) ⇒ hasN extOperator(?r, ?x)

(2) It is worth noting, that separating the rules in this way also favors reusability because the results of the individual more granular rules can be used by other rules. 5 .

B. More complex collaborative policies

More elaborate policies than the one described above are typically required. An example of a more complex policy could be the following:

Policy 2 : Give operator status to learners upon request only if they have less accumulated operating time than the current user. This restriction does not apply to teachers and administrators, who are granted preemptive access.

Again, this policy can be expressed as a set of SWRL rules:

                                                                  
Def hasAdminOperator : RemoteLab(?r) ∧ Admin(?x) ∧ hasP endingOperatingRequest(?x, ?r) ⇒ hasN extOperator(?r, ?x) Def hasT eacherOperator : RemoteLab(?r) ∧ T eacher(?x) ∧ hasP endingOperatingRequest(?x, ?r) ⇒ hasN extOperator(?r, ?x) Def hasStudentOperator : RemoteLab(?r) ∧ hasOperator(?r, ?s1) ∧ Student(?s2) ∧ hasP endingOperatingRequest(?s2, ?r) ∧ hasOperatingP eriod(?s1, ?op1) ∧ hasOperatingP eriod(?s2, ?op2) ∧ hasDuration(?op1, ?d1) ∧ hasDuration(?op2, ?d2) ∧ swrlb : lessT han(?d2, ?d1) ⇒ hasN extOperator(?r, ?s2)

(3) Other possible collaborative policies include:

• When Bob requests the device control, he is granted the operator status if Bob has less operating time than Alice.

• When Bob requests the device control, he is granted the operator status if it is the first time that he has requested it.

• When Bob requests the device control, he is granted the operator status if he was connected before Alice.

• When Bob requests the device control, it is refused to him if he had more than 5 disconnections in the current session.

• When Bob requests the device control, he is granted the operator status only if he has entered the session more than 10 minutes ago.

• The users are granted a preemptive and dedicated access of 15 minutes. Within those 15 minutes they cannot be preempted by other users (except by administrators and teachers), that also means they cannot preempt other users when it is not their time slot. Many more policy rules than those presented here are possible and these rules can be combined and reused in many ways to achieve a tutor's goals. Even though our underlying domain ontology is relatively simple, considerable expressivity is afforded with the use of SWRL rules. As can be seen, time plays an important role in most policy rules. Our policy rules make extensive use of a SWRL temporal library6 . The collaborative logic and knowledge base are implemented by a Collaborative Unit Logic module (see Fig. 3). This module provides:

V. IMPLEMENTATION OF

• Connections between a collaborative online laboratory and collaborative policies

• All the knowledge related to collaboration and the current state of a session

• Updates knowledge upon new actions over the online laboratory

• Decides, according to the collaborative policy in place, whether or not a user requesting the operator status is granted it or not. The system implementation relies on widely available, open source Semantic Web software and APIs. The domain ontology was developed using Protégé-OWL and the SWRL policy rules were developed and executed using its SWRLTab plugin [START_REF] O'connor | Supporting Rule System Interoperability on the Semantic Web with SWRL[END_REF].

B. Switching collaborative policies in an online session

Clearly, all sessions within an online laboratory cannot use the same collaborative policy. Even within a single session, the tutor may want to change the policies. For these reasons, we introduce an Adaptive Module, which is responsible for switching from one rules set to another. At the application level, this process involves dynamically switching policy rule sets. This switching process must maintain the current system state.

A possible scenario is as follows: Mr. Smith sets the collaborative policy to the administrator policy so that he can prepare the session for Bob and Alice. Bob and Alice can join the session and see Mr. Smith's actions but they are not allowed to perform any actions themselves. Afterwards, as Mr. Smith wants both Bob and Alice to test the COL, he sets the collaborative policy to favor late comers. The system discards the previous collaborative policy rule set and loads the set representing the new policy. Later, to avoid too much inequality in the access during the session, Mr. Smith sets the collaborative policy to favor users with less operating time. Again, the system has to seamlessly switch from one rule set to another. It should be noted that this approach to implementing collaborative policies is not specific to online laboratories, and thus could be used by other knowledge-based systems that require similar policy mechanisms.

OWL's monotonic inference mechanism must be considered when switching rule sets. Because SWRL rules are logically part of an OWL ontology, retracting one set of rule and asserting another can introduce nonmonotonicity. Designers of adaptive systems that use OWL need to deal with this issue. A possible solution is to discard all current session knowledge when policies are switches, but this approach is clearly unsatisfactory. Ideally, the adaption process should preserve the original context when dynamically loading new rules that encode different policies. A solution that we have adopted is to explicitly identify assertions made by policy rules and to associate a temporal scope or dimension with them to identify their period of validity. Any further assertions made using these assertions must be similarly scoped. When a new policy is activated, these assertions and any further assertions made using them are no longer valid. This solution ensures that no inconsistencies are introduced by rule set switching but it is not a general solution for all adaptive systems. Developers of such systems may need to develop custom solutions for their domain, though the temporal scoping mechanism outlined here is quite robust for this specific system.

VI. EVALUATION

To evaluate user satisfaction with the system, twelve threehour sessions were arranged. Each session involved 45 students, divided into groups of four (with one oversized group of 5 students). Students in each group were connected to one remote workbench device using a single touch screen. Users remotely handled the workbench while facing questions on the purpose for their hands-on session. The logging system shows that an average of 95 commands were relayed per hour per group of students.

One of the main objectives of online laboratories is to offer a learning experience as close as possible to reality. In particular, the device's HCI is expected to be an accurate facsimile of the physical device's interface. Frequently, learning to use the HCI of a specific piece of equipment is a pedagogical objective itself. In general, HCI quality tend to strongly affect user perception of the overall quality of the collaborative online laboratory experience. Hence, the survey also evaluated user satisfaction with the online device's HCI in addition to evaluating users' overall perception of the system.

The survey questionnaire was dividen into two sections:

• To evaluate the HCI, we generated a user interface evaluation questionnaire using commonly available web software [START_REF] Perlman | Web-Based User Interface Evaluation with Questionnaires[END_REF]. Three common evaluations heuristics were used in the generation this questionnaire: Nielsen's Attributes of Usability7 , Lewis' works on IBM Computer Usability Satisfaction Questionnaires8 , and Chin's Questionnaire for User Interface Satisfaction9 .

• The second part of the questionnaire was dedicated to the learning experience itself. The list of survey questions is accessible online [START_REF] Gravier | Vers la généralisation de manipulations distantes et collaboratives d'instruments de haute technologie[END_REF].

A. Survey Results

In general, participants thought that a collaborative online laboratory was a very good idea (82%). In response to the question "In your opinion, is it important to collaborate with other people?", more than half (58.82%) thought it was useful to help one another, 32.35% enjoyed comparing their experimental results with other people, and the remaining (8.82%) used it to speak about student parties they had (sic!). They also noted (92.60%) that using a collaborative online laboratory instead of a local laboratory helped them significantly when writing reports, particularly when reproducing result graph. Each of the three HCI evaluation heuristic give approximately the same result for user satisfaction for device usability: 62.04% for questionnaire based on Nielsen's work, 59.14 for the one based on Lewis's, and 57.50% for questionnaire from Chin's works (greatest difference is 4.54%). 55.56% of participants assessed the graphical user interface to be very close to the real interface of the remote device, and 40.47% noticed it exhibited only a few differences. The overall performances of the platform were judged satisfactorily by 77.78% of users, even by those using slow laptops (with Intel Celeron processors and 256 Mb of RAM). It is interesting to note that 62.96% of students intentionally attempted actions that were not forecasted by the hands-on session questions, driven primarily by curiosity.

B. Informal results

As mentioned, the teachers were in general satisfied with the system. They stressed three main points:

• The platform allowed them to satisfactorily perform an online laboratory. For most of the teachers, this was the first time that they has participated in such a laboratory. Most saw it as a positive pedagogical experience that contributed to their knowledge of distance learning.

• Teachers tend to put high importance on group awareness and appreciated that the platform allowed them to clearly see which students are more active than others. This requirement is naturally performed in local laboratories where teachers can easily identify weak or strong students. Since distance can introduce a significant decrease of awareness of other persons, group awareness support can be key in compensating for this deficit. Teachers, however, stressed that they may also have to develop new group awareness skills when managing online laboratories.

• Many-to-many online laboratories were also recognized as a way to encourage students to exchange results and support peer assistance in addition to allowing teachers to manage more students than a point-to-point online laboratory.

VII. CONCLUSION

Our solution supports the development of collaborative policies that reflect the pedagogy that a tutor wishes to promote in collaborative online learning sessions. These collaborative policies are described using SWRL rule sets, which are written on top of an OWL domain ontology that describes the core entities in collaborative online laboratories. These rules encode policies allow the system to determine per user access control policies for devices being used in a session. These access policies reflect the pedagogical goals of a tutor. We have described an implementation of the system and outlined an evaluation. While the ontology and associated policy rule sets is specific to the collaborative online learning domain, the architecture and approach may be generalized to support other adaptive systems for computer-supported collaborative applications. Future work consists in trace mining of events during an online session to measure the quality of collaboration during the session. This mining can be used to identify possible leaders, for example, or to identify weak students who may require more assistance. The underlying challenge is to enhance the learning experience of users in collaborative online learning laboratories to meet the goals of both tutors and students.

Fig. 1 .

 1 Fig. 1.Real laboratory (top) and associated software (bottom). The equipment is a Vector Network Analyzer. It is used to measure parameters of an electronical network to determine its signal transmission and reflection capacities.

Fig. 2 .

 2 Fig. 2. A simplified view of the ontology of collaborative online laboratory events (light background indicates primitive classes, darker background indicates defined classes).

Fig. 3 .

 3 Fig. 3. Implementation of the collaborative process unit.

Manuscript received March 31st, 2009; revised Month, Day, Year. C. Gravier (@chgravier), J. Fayolle, and J. Lardon are with the Université de Lyon, F-42023, Saint-Etienne, France, Université de Saint-Etienne, Jean Monnet, F-42023, Saint-Etienne, France, Télécom Saint-Etienne, SATIN, équipe associée de l'Institut TELECOM, DIOM, EA3523, F-42023, Saint-Etienne, France.M. O'Connor is with Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, U.S.A.

http://diom.telecom-st-etienne.fr/satin/einst/einst demo.avi

Semantic Web Rule Language, http://www.w3.org/Submission/SWRL/

This ontology can be downloaded from http://dev.telecom-st-etienne.fr/ satin/rlab/collaborativev4.swrl.owl.

An introduction to the SWRL language can be found at: http://protege. cim3.net/cgi-bin/wiki.pl?SWRLLanguageFAQ.

These rules all other rule sets described in this paper are available at: http://dev.telecom-st-etienne.fr/satin/rlab/policies/rules.zip.

http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTemporalBuiltIns

http://hcibib.org/perlman/question.cgi?form=NAU

http://hcibib.org/perlman/question.cgi?form=CSUQ

http://hcibib.org/perlman/question.cgi?form=CSUQ

ACKNOWLEDGMENT

This work is funded by the Conseil Général de la Loire, France: http://www.loire.fr/. Mr. O'Connor was supported in part by grants LM007885 and LM009607 from the U.S. National Library of Medicine.


Baptiste Hemery

Hélène Laurent

Christophe Rosenberger

Subjective evaluation of image understanding results


A study of users' acceptance and satisfaction of biometric systems

Mohamad El-Abed, Romain Giot, Baptiste Hemery and Christophe Rosenberger GREYC Laboratory, ENSICAEN -University of Caen Basse-Normandie -CNRS ENSICAEN, 6 boulevard marechal Juin 14050 Caen Cedex 4 FRANCE Email: {mohamad.elabed, romain.giot, baptiste.hemery, christophe.rosenberger}@greyc.ensicaen.fr Abstract-Biometric authentication methods are being increasingly used for many types of applications. Since such methods necessitate humans to interact with a device, effective implementation requires consideration of the perceptions and responses of end users. Towards this goal, we present in this paper a modalityindependent evaluation methodology to study users' acceptance and satisfaction of biometric systems. It is based on the use of a questionnaire and some data mining tools for the analysis. We have applied it on two biometric systems developed in our research laboratory. The results from this study demonstrated that users' satisfaction analysis should be more taken into account when developing biometric systems. A significant panel of 70 users was more satisfied from the keystroke system than the other one. Users surprisingly considered that its perceived performance was also better even if the used face system has a better performance with an EER of 8.76% than the keystroke one with an EER of 17.51%. The robustness of a system against attacks and its perceived trust have been identified as important factors to take into account when designing biometric systems. Results have also demonstrated significant relationships between demographic factors and their perception about the biometric technology and the studied systems.

Index Terms-evaluation of biometric systems, users' acceptance, Kruskall-Wallis test, Bayesian networks, Decision Trees.

I. INTRODUCTION

Biometrics offers automated methods for identity verification and identification based on physiological, morphological or behavioral characteristics. By contrast with possessionbased ("what we own" such as a key) or knowledge-based ("what we know" such as a password), biometrics is based on "what we are" and "how we behave". Many biometric systems have been proposed in the literature for the last decade [START_REF] Chen | Beyond minutiae: A fingerprint individuality model with pattern, ridge and pore features[END_REF], [START_REF] Mendez | Face recognition with lwir imagery using local binary patterns[END_REF]. They are mainly used to manage the access of physical (e.g., airports) and logical (e.g., e-commerce) resources. Evaluating biometric systems constitutes one of the main challenges in this research field. Nowadays, many studies have been done to evaluate such systems. Evaluation is generally realized within four aspects as illustrated in Fig. 1: • performance: defines some quantitative metrics to measure the efficiency of biometric systems [START_REF]Information technology biometric performance testing and reporting[END_REF] such as equal error rate (EER), failure-to-enroll rate (FTE), computation time . . . ; • acceptability and user satisfaction: measures users' perception, feelings and opinions regarding the system [START_REF] Theofanos | Usability & Biometrics: Ensuring Successful Biometric Systems[END_REF];

• data quality: measures the quality of the biometric raw data. Low quality samples increase the enrolment failure rate, and decrease the system performance; • security: measures how well a biometric system (algorithms and devices) can resist to several types of attacks. Different attacks have been reported and analyzed in [START_REF] Uludag | Secure biometric systems[END_REF] such as presenting a fake (e.g., dummy finger), replay attack, Denial of Service (DoS) . . . The evaluation of biometric systems is now carefully considered. Many databases have been collected (such as UBIRIS [START_REF] Proenc | UBIRIS: A noisy iris image database[END_REF]), many competitions (such as Fingerprint Verification Competition [START_REF] Maio | Fvc2004: Third fingerprint verification competition[END_REF]) and platforms have been proposed (such as BioSecure [START_REF] Petrovska | Description and documentation of the biosecure software library[END_REF]) whose objective is mainly to compare enrollment and verification/identification algorithms in the state of the art. Multiple metrics are used within this context [START_REF]Information technology biometric performance testing and reporting[END_REF]. These statistical measures allow in general a precise performance characterization of a biometric system. Nevertheless, these works are dedicated to quantify performance technology (algorithms, processing time, memory required . . . ) without taking into account users' perception when using these systems. One important challenge is that a biometric system is human centric [START_REF] Kukula | Human-biometric sensor interaction: Impact of training on biometric system and user performance[END_REF]. The authors in [START_REF] Jain | Biometrics: A grand challenge[END_REF] illustrate the complexity of designing a biometric system on three main factors: (i) accuracy in terms of errors, (ii) scale or size of the database and (iii) usability in terms ease of use, acceptability . . . Therefore, studying and analyzing users' perception constitutes a key factor to make it operational and acceptable. One government can decide that an individual would be identified through a biometric data embedded in the passport. For logical or physical access control in a company, it is more difficult to impose a system that would be not accepted by users. As for example, DNA analysis is one of the most efficient techniques to verify the identity of an individual or to identify him. Nevertheless, it cannot be used for logical or physical access control not only for time computation reasons, but also because nobody would be ready to give some blood to make the verification.

Nowadays, there is a lack of a generic evaluation methodology that takes into account users' perception within the evaluation process, which constitutes one of the main drawbacks for biometric systems proliferation. For this purpose, we propose in this paper a modality-independent evaluation methodology to study users' acceptance and satisfaction of biometric systems. Such kind of evaluation, in our opinion, will: (i) enhance the performance of biometric systems [START_REF] Kukula | Effect of human-biometric sensor interaction on fingerprint matching performance, image quality and minutiae count[END_REF], (ii) improve the accuracy of the optimistic results provided by biometric system designers in terms of errors (e.g., EER) and (iii) reduce product complexity and increase user satisfaction.

The paper is organized as follows. In section II, we present the background of several works done to understand the different factors affecting users' acceptability and satisfaction. Section III details the proposed methodology based on user survey. Experimental results of the proposed methodology are given in section IV. We give a conclusion and propose some perspectives of this work in section V.

II. BACKGROUND

Understanding human perception can be exploited in different domains. Human perception is quite predictable in many instances and can be understood with a high degree of accuracy. Extracting such information aims to add the community in biometrics to: (i) more understand the needs of users and (ii) improve the quality of biometric systems (algorithms and devices). Nowadays, several studies have been done to quantify users' acceptability and satisfaction of biometric systems such as:

• NIST Biometrics Usability group has performed a usability test on fingerprints [START_REF] Theofanos | Usability testing of ten-print fingerprint capture[END_REF]. The survey was conducted on 300 adults recruited from a pool of 10,000 people. For privacy issues, 87% in 2001 and 88% in 2002 are worried for the misuse of personal information. There is a good percentage of acceptance, more than 75%, for U.S. law enforcement authorities requiring fingerprint scans to verify identity for passports, at airport check-ins and to obtain a driver license (see [START_REF]Public Attitudes Toward the Uses of Biometric Identification Technologiesby Government and the Private Sector[END_REF] for more details). • Other studies presented in [START_REF] Giot | Keystroke dynamics authentication for collaborative systems[END_REF], [START_REF] Deane | Perceived acceptability of biometric security systems[END_REF], [START_REF] Coventry | Honest it's me! self service verification[END_REF], [START_REF] Schimke | Cross cultural aspects of biometrics[END_REF], [START_REF] Moody | Public perceptions of biometric devices: The effect of misinformation on acceptance and use[END_REF], [START_REF] Jones | Towards understanding user perceptions of authentication technologies[END_REF], [START_REF] Elliott | The perception of biometric technology: A survey[END_REF], [START_REF] Heckle | Perception and acceptance of fingerprint biometric technology[END_REF], [START_REF] Pons | Understanding user perspectives on biometric technology[END_REF], [START_REF] Riley | Instruction, feedback and biometrics: The user interface for fingerprint authentication systems[END_REF] have highlighted several points about biometrics such as:

acceptance is linked to the number of uses of the biometrics in general and information provided by the biometric device can also improve user acceptance; there is a potential concern about the misuse of personal data (i.e., templates) which is seen as violating users' privacy and civil liberties. Another important concern is the probability that criminals may perpetrate heinous acts to gain access. This could include stalking or assaulting individuals to steal their biometric information; individuals complain that once the biometric template is stolen, it is compromised forever; there are also concerns about hygiene with touching such devices and health risks for more advanced technologies such as iris or retina. According to our knowledge, none paper has emphasized physical harm to users of these systems. But despite of this, several concerns were highlighted along this interaction. Anecdotally, some users of biometrics have complained that hand geometry systems dry their hands while military aviators participating in an experimental program voiced concern that retinal scanning would damage their vision with extended use over time.

A. Discussion

Studies presented in the previous section highlighted different important factors that have to be taken into account when studying users' perception such as:

• socio-demographic factors: such as age, gender, ethnicity, religion, experience and ability; • learnability and memorability: they mainly concern how rapidly a user can use the system after instruction or training; • confidence or trust: indicates how the performance of the system is perceived by users. It depends mainly on feedbacks from users and their experience; • ease to use: depends on the quality of the biometric sensor and the ergonomic interface. It may also depend on the time required for verification or identification. For example, if the biometric system takes several minutes between the acquisition of the required data and user identification, users may believe that the biometric system is not easy to use; • Privacy issues: there is a potential risk concerning the misuse of the personal collected data, which is seen as violating user's privacy and civil liberties. Many debates have been conducted over the central storage of biometric templates versus holding the personal template on a smart card where the verification is locally processed; • Physical invasiveness: the acquisition of biometric data requires user interaction with the biometric sensor. Depending on the used method, acquisition of biometric data is performed with or without contact with the biometric sensor; • Cultural issues: the acceptability denotes the way how users perceive the biometric system and interact with it. Acceptability is highly dependent of the culture of users. As for example, cultures with an aversion to touch public surfaces would prefer to use biometric systems with contactless sensors (e.g., iris or palm veins). Studies done on evaluating users' acceptability and satisfaction of biometric systems are very few in comparison with performance ones. Moreover, these studies are based on statistical answers to a questionnaire but no serious data analysis is conducted for understanding the reasons. In order to contribute to solve this problem, the goal of this paper is to propose a methodology that studies users' perception to enhance the usability of biometric systems. This evaluation methodology is modality-independent (i.e., it could be applied on any kind of biometric systems). As for us, taking into account users' view of the biometric process (hardware, software and instructional design) is not only beneficial to the end users but it will also help to improve the performance and effectiveness of a system [START_REF] Kukula | Effect of human-biometric sensor interaction on fingerprint matching performance, image quality and minutiae count[END_REF].

III. EVALUATION METHODOLOGY

The proposed methodology was designed to quantify users' satisfaction on the use of biometric systems. To accomplish this objective, we developed a survey instrument for data collection. These kinds of surveys enable to gather information to be statistically analyzed. The proposed methodology principle is illustrated in Fig. 2: It collects the data using a questionnaire (section III-A). This step is followed by a pre-processing phase to extract the significant knowledge (section III-B). Then, the Kruskall-Wallis test is performed to determine if there is a significant relationship between demographic characteristics and respondents' answers (section III-C). Data mining tools are used to explain these answers to determine the possible candidates that affect their acceptance and satisfaction of biometric systems (section III-D).

A. Data collection

The data collection phase is based on a survey instrument (see Appendix). It was designed to collect demographic, Fig. 2. Methodology principle experiential and attitudinal characteristics that might have an impact on or a relationship to respondents' views on the use of biometrics. The survey takes into account other works in the field done by NIST [START_REF] Theofanos | Usability testing of height and angles of ten-print fingerprint capture[END_REF], NCR [START_REF] Coventry | Usability and biometric verification at the atm interface[END_REF], ORC [START_REF]Public Attitudes Toward the Uses of Biometric Identification Technologiesby Government and the Private Sector[END_REF], Atos origin [START_REF]Uk passport service biometrics enrolment trial[END_REF] and other works presented in [START_REF] Kukula | Implementation of hand geometry at purdue university's recreational center: an analysis of user perspectives and system performance[END_REF], [START_REF] Jones | Towards understanding user perceptions of authentication technologies[END_REF], [START_REF] Moody | Public perceptions of biometric devices: The effect of misinformation on acceptance and use[END_REF], [START_REF] Elliott | The perception of biometric technology: A survey[END_REF]. It also noted unsolicited questions that we found it valuable when collecting the GREYC-Keystroke database [START_REF] Giot | Greyc keystroke : a benchmark for keystroke dynamics biometric systems[END_REF]. A 4point Likert-type scale is used to valuate the answers on the perception questions. The survey questionnaire contains 18 questions divided into two sets:

• general perception of biometric systems (Appendix, part B) which contains 7 questions aiming to understand users' experience on biometric technology; • perception of the tested system (Appendix, part C) which contains 11 questions aiming to measure users' satisfaction and acceptance on the tested system.

In addition to these questions, we request some information on the individual: gender, age . . . (Appendix, part A). These demographic characteristics are requested to determine if there is significant relationships between them and respondents' perception on biometric technology. Also, we propose the question 16 (Appendix, part C) to identify where the use of the tested system would be appropriate.

B. Data Pre-processing

Prior to analyzing the pilot data, we use a technique to enhance the accuracy and the reliability of the extracted knowledge. It consists of deleting answers having a predefined number of gaps (i.e., questions without any answers).

C. Respondent demographics analysis

In order to determine whether there is a significant relationship between demographic characteristics and respondents' perception on biometric technology, we use the Kruskall-Wallis test (KW). It is a nonparametric (distribution free) test, which is used to decide whether K independent samples are from the same population. In other words, it is used to test two hypothesis given by Eq. 1: the null hypothesis H 0 assumes that samples originate from the same population (i.e., equal population means) against the alternative hypothesis H 1 which assumes that there is a statistically significant difference between at least two of the subgroups.

H0 : µ 1 = µ 2 = ... = µk H1 : µ i = µ j (1)
The Kruskal-Wallis test statistic is given by Eq. 2 and the p-value is approximated, using chi-square probability distribution, by Pr(χ 2 g-1 ≥ H). The decision criterion to choose the appropriate hypothesis is defined in Eq. 3.

H = 12 N(N + 1) g ∑ i=1 n i r2 i. -3 (N + 1) (2)
where n i is the number of observations in group i, r i j is the rank of observation j from group i, N is the total number of observations across all groups.

r2 i. = ∑ n i j=1 r i j ni and r = 1 2 (N + 1) p -value ≥ 0.05 accept H 0 otherwise re ject H 0 (3)

D. Data mining analysis

In order to analyze respondents' answers, we use two types of classifiers: Bayesian networks [START_REF] Friedman | Bayesian network classifiers[END_REF] and Decision Trees [START_REF] Breiman | Classification and regression trees[END_REF]. They are formal graphical tools for representation of decision scenarios requiring reasoning under uncertainty.

1) Bayesian networks: A Bayesian network (B S , B P ) is a probabilistic graphical model that represents a set of random variables U = {x 1 , x 2 , ...x n } and their conditional independencies. The Bayesian structure B S , is a directed acyclic graph (DAG) where nodes represent propositional variables in a domain, and the arcs between nodes represent the dependency relationships among the variables. The Bayesian probability distributions B P , is a set of probability tables B P = {p(u|pa(u)) |u ∈ U} where pa(u) is the set of parents of u in B S . The method used to learn the bayesian network structure B S is based on conditional independence tests as described in [START_REF] Bouckaert | Weka manual[END_REF]. This method mainly stem from the goal of uncovering causal structure. The assumption is that there is a network structure that exactly represents the independencies in the distribution that generated the data. The method is divided into two stages:

• find a skeleton: starting with a complete undirected graph, the method try to find conditional independencies {x → y} ∪ ∀z ∈ Z z → y in the data. If a, independency is identified, the edge between x and y is removed from the skeleton. To test whether variables x and y are conditionally independent given a set of variables Z, a network structure with arrows ∀z ∈ Z z → y is compared with one with arrows {x → y} ∪ ∀z ∈ Z z → y. A test is performed by using a predefined score metric. In this study, we use four score metrics as defined below. We use the following conventions to identify counts in the database D of a network structure B S . Let r i (1 ≤ i ≤ n) be the cardinality of the variables x i . We denote by q i the cardinality of the parent set of x i in the network structure B S . Hence, q i can be calculated as the product of cardinalities of nodes in pa(x i ), q i = ∏ x j ∈pa(x i ) r j . We denote by N i j (1 ≤ i ≤ n, 1 ≤ j ≤ q i ) the number of records in D for which pa(x i ) takes its jth value. We denote by

N i jk (1 ≤ i ≤ n, 1 ≤ j ≤ q i , 1 ≤ k ≤ r i )
the number of records in D for which pa(x i ) takes its jth value and for which x i takes its kth value. Hence, N i j = ∑ r i k=1 N i jk . We use N to denote the number of records in D.

-Entropy metric H(B S , D) defined as

H(B S , D) = -N n ∑ i=1 q i ∑ j=1 r i ∑ k=1 N i jk N log N i jk N i j ( 4
)
-AIC metric Q AIC (B S , D) defined as

Q AIC (B S , D) = H(B S , D) + K ( 5
)
where

K = ∑ n i=1 (r i -1).q i -MDL metric Q MDL (B S , D) defined as Q MDL (B S , D) = H(B S , D) + K 2 log N (6)
-Bayesian metric Q Bayes (B S , D) defined as

Q Bayes (B S , D) = n ∏ i=0 q i ∏ j=1 (r i -1)! (r i -1 + N i j )! r i ∏ k=1 N i jk ! (7)
• direct acyclic graph (DAG): the second stage consists in directing all the edges in the skeleton to get a DAG. The first step in directing arrows is to check for every configuration x --z --y where x and y not connected in the skeleton whether z is in the set Z of variables that justified removing the link between x and y. If z / ∈ Z, we can assign direction x → z ← y. Then, a set of rules is applied to direct the remaing edges: 2) Decision trees: Introduced in 1984 by Breiman et al. [START_REF] Breiman | Classification and regression trees[END_REF], decision trees are one of the few knowledge representation schemes which are easily interpreted and may be inferred by very simple learning algorithms [START_REF] Baldwin | Simple fuzzy logic rules based on fuzzy decision tree for classificationand prediction problem[END_REF]. A decision tree is a tree in which: (i) each internal node tests an attribute, (ii) each branch corresponds to attribute value and (iii) each leaf node assigns a classification. Decision trees are potentially powerful predictors and provide an explicit concept description for a dataset.

-rule 1: if i → j --k & i -/ -k then j → k -rule 2: if i → j → k & i --k then i → k -rule 3: if i → j ← k & S 1 then m → j -rule 4: if i → j & S 2 then i → m & k → m -
Nowadays, several methods have been proposed for constructing decision trees. For this study, we have used the most used methods in the state in the art such as C4.5 [START_REF] Quinlan | C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning[END_REF], CART [START_REF] Breiman | Classification and regression trees[END_REF] and BFTREE [START_REF] Shi | Best-first decision tree learning[END_REF]. We have tested these algorithms on our dataset and we found that C4.5 algorithm outperformed the others. Therefore, we will present a brief description of this decision trees algorithm used in our study.

C4.5 [START_REF] Quinlan | C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning[END_REF] is an extension of the ID3 [START_REF] Quinlan | Induction of decision trees[END_REF] algorithm developed by Ross Quinlan in 1986. C4.5 builds decision trees from a set of training data using the concept of information entropy defined in Eq. 8. At each node of the tree, C4.5 chooses one attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. Its criterion is based on the gain ratio, defined in Eq. 12, that results from choosing an attribute for splitting the data. The attribute with the highest gain ratio is chosen to make the decision. The C4.5 algorithm then recurses on the smaller sublists.

Entropy(p) = - c ∑ k=1 P(k/p) × log(P(k/p)) (8)
where:

• N(p) is the cardinality of the set of observations associated to the position p in the database of observations D; • N(k/p) is the cardinality of the set of observations associated to the position p belonging to the class k; • P(k/p) is defined as:

P(k/p) = N(k/p) N(p) (9) Gain(p,test) = Entropy(p) - n ∑ i=1 P i × Entropy(p i ) (10) SplitIn f o(p,test) = - n ∑ i=1 P′(i/p) × log(P′(i/p)) (11)

GainRatio(p,test) = Gain(p,test) SplitIn f o(p,test) (12)

3) Performance metrics: Classifiers are useful tools which are commonly used in decision analysis, to help identifying a strategy most likely to reach a goal [START_REF] Edwards | Clustering and classification of maintenance logs using text data mining[END_REF]. They provide a highly effective and simple structure that can be explored to make predictions and decisions. Despite the obvious advantages of these tools, they do not provide a 100% accuracy result.

Due to this inaccuracy, several performance criteria have been proposed in the state of the art [START_REF] Ricco | Graphes d'induction[END_REF] to identify the quality of a classifier. The main criteria used are:

• Accuracy: denotes the percentage of the correctly classified instances; • Area under the ROC curve (AUC): it is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. In our study, AUC is estimated using Mann-Whitney statistic test as presented in [START_REF] Ling | Auc: A better measure than accuracy in comparing learning algorithms[END_REF]. The AUC of a classifier G is defined as:

AUC = S 0 -n 0 (n 0 + 1)/2 n 0 1 ( 13
)
where n 0 and n 1 are the numbers of positive and negative examples respectively, and S 0 = ∑ r i , where r i is the rank of the ith positive example in the ranked list. The authors in [START_REF] Ling | Auc: A better measure than accuracy in comparing learning algorithms[END_REF] suggest that its use should replace accuracy when measuring and comparing classifiers: the best classifier is the one with the largest AUC; • comprehensibility: qualifies the exploitability of the produced model. For example, in a Bayesian network, the important number of a node's parents affects the identification of its strong relations with them; • classification rapidity: which also would be a crucial factor if the training dataset is huge for example. Since the size of our dataset is not important (less than 100 records, 1 record for each respondent), we have used only the following metrics to determine the best produced model, from the two classifiers, that fits our dataset: Accuracy, AUC and comprehensibility.

IV. EXPERIMENTAL RESULTS

In this section, we analyze respondents' answers to extract some knowledge about their perception on the two studied systems: keystroke dynamics and face verification systems. In order to perform Kruskall-Wallis tests, Decision Trees and Bayesian networks, we have developed an application that uses the WEKA library [START_REF] Bouckaert | Weka manual[END_REF]. In the next section, we present the volunteer crew that participated in this study.

A. Test protocol

The pilot study was distributed on a paper sheet to a sample of 70 volunteers, including students (71.4%) coming from different countries and employees (28.6%). Tests have been conducted in public places over a 2 months period. It consists in testing both systems (enrollment then multiple verifications playing the role of an impostor and a legitimate user). Then, they were requested to answer a questionnaire: part A, part B and two times part C (one for each system). Volunteers completed the survey voluntarily and received none remuneration. During the tests, volunteers were informed about the purpose of the study, and their responses would be confidential and anonymous. The age and gender distribution of the volunteer crew are shown in Fig. 4.

B. Test materials

In this study, we have used two biometric systems. Their performances are calculated with captures provided by our volunteer crew. We plot their Detection Error Trade-off (DET) curves in Fig. 5: • Keystroke verification system: It is a biometric system based on behavioral analysis developed in our research laboratory [START_REF] Giot | Keystroke dynamics authentication for collaborative systems[END_REF]. The main goals of this software is to allow the creation of a keystroke dynamics database [START_REF] Giot | Greyc keystroke : a benchmark for keystroke dynamics biometric systems[END_REF] and to compare different algorithms in the state of the art, within the same conditions (e.g., acquisition conditions), for evaluation issues. The system provides an EER value equals to 17.51% on a database composed of 70 individuals with 3 vectors used for enrollment and 2 for the tests. The system implements a score-based method presented in [START_REF] Hocquet | User classification for keystroke dynamics authentication[END_REF]. It is a statistical method based on the average (µ) and standard deviation vectors (σ ) computed with the enrollment vectors, with v a test vector of n dimensions:

score = 1 - 1 n n ∑ i=1 exp - |v i -µ i | σ i ( 14
)
• Face verification system: It is a biometric system based on morphological analysis developed in our research laboratory. The system uses comparisons based on keypoints detection using SIFT descriptors. It provides an EER value equals to 8.76% on a database composed of 70 individuals with 1 image used for enrollment and 2 for the tests.

C. Data pre-processing

The first step of the proposed methodology consists in the deletion of respondents' answers that did not answer a certain number of questions of the questionnaire. For 3 unanswered questions, two vectors of answers (one from keystroke verification system and the other from face one) have been eliminated from this study. Therefore, the results presented in the next sections are done using 69 vectors of answers on both systems.

D. Respondent demographics analysis

We study in this section the relationship, on each system, between respondents' demographic characteristics and their answers on perception questions. Table I shows the results for a confidence degree = 95%. Bold values indicate significant relationships based on the criterion defined in Eq. 3. From this table, we can put into obviousness these significantly relationships:

• gender was significantly related to their knowledge about biometric technology: males have expressed more knowledge than females; • age was significantly related to their answers about keystroke's robustness against attacks: aged respondents (≥ 28) considered that the system is more robust against attacks than youngest ones; • education level was significantly related to their opinions about secret-based solutions against fraud: high school graduate respondents found that secret-based solutions are less appropriate against fraud than college graduate ones; • education level was significantly related to the disturb factor while using both systems: none graduated respondents were much more disturbed while using face verification system than the others. For keystroke system, they were much more disturbed than high school graduate respondents; • education level was significantly related to their concerns about privacy issues while using keystroke system: none graduated respondents have expressed much more concerns about their privacy than the others; • education level was significantly related to their willingness to use the face system in the future: college graduate respondents have expressed more willingness to use it than the others; • education level was significantly related to their trust on face system: high school graduate respondents have expressed less trust on it than the college graduate ones.

E. Comparative study of the studied systems

In this section, we present respondents' knowledge about biometric technology and a comparative analysis between the studied systems based on a statistical analysis of their answers. A Kruskall-Wallis test was performed to identify the significant differences among this comparison. Table II shows the results for a confidence degree = 95%. Bold values indicate significant relationships based on the criterion defined in Eq. 3. From the answers given by respondents and table II, we can put into obviousness some interesting points:

• most of the respondents (72.5%) have already heard before our study of biometric authentication systems and less than half of them (43.5%) have already used a biometric system; • 43.5% of the respondents have expressed a good knowledge about biometric technology; • using Kruskall-Wallis test (p-value < 0.01), respondents considered that biometric technology (92.75% agree) is much more appropriate than secret-based solutions (39.13% agree) against fraud; • there were no significant differences on disturbed, easy to use, willingness to use the system in the future and trust factors. 21.74% were disturbed while using face system and 13.04% for keystroke one. 11.6% of the respondents have found that face system is not easy to use and 8.7% for keystroke one. 27.54% of the participants hesitate or refuse the use of face system in the future and 15.94% for keystroke one. For their perception about trust, 33.33% do not trust face system and 23.19% for keystroke one; • there were significant differences about their concerns for privacy issues, their perception about systems performances and their general appreciation among the studied systems. Respondents have expressed much more concerns about their privacy while using face system (46.4%) than keystroke one (13.04%). They found that keystroke performance outperformed face one. For their general appreciation, they were more satisfied from the use of keystroke system (89.85%) than face one (81.16%); • finally, 26.1% prefer to use the face system and 56.52%

for keystroke one for managing logical access, 36.23% prefer to use the face system and 14.5% for keystroke one for physical access, 31.88% prefer to use the face system and 26% for keystroke one for both kinds of access. This indicates that the keystroke system is more requested to be used for managing logical access, while the other one for physical access.

F. Discussion

The results of this comparative study and the statistical analysis of answers brought many interesting information. We found a frustrating rate (46.4%) concerning their concerns about privacy issues while using face system. The results also brought surprising rates concerning systems performance and their general appreciation. Respondents found that keystroke's performance outperformed face one and they were more satisfied from the keystroke system than the face one. Therefore, it would be important to explain respondents' answers to more understand these rates and the significant differences among the studied systems. This is what we present in the next section.

G. Data mining analysis

The purpose of this section is to study the dependences between perception questions, Q i f or i = 1 : 17, to understand respondents' answers and perception. We would also like to more understand the surprising rates provided by the previous section. Due to the nature of construction of Decision Trees and Bayesian networks, missing values (i.e. questions without answers) are handled for both kinds of attributes. For nominal attributes, they are replaced by the most frequently one. While for numerical ones, they are replaced by the average one. Using Bayesian networks and Decision Trees, several points can be concluded:

• Using table III, respondents' concerns about their privacy while using the face system can be explained by their willingness to use the system in the future and their perception about its robustness against attacks. From the respondents that have expressed concerns about their privacy, 27.6% of them hesitate or refuse its use in the future and 31% found that it can be easily attacked. Since most of the respondents (63.77%) do not found the system robust against attacks and 27.54% hesitate or against its use in the future, this explains why a lot of respondents (46.4%) have expressed such concerns; • Respondents' perception about keystroke performance was related to their general appreciation and if they felt disturbed while using it (Fig. 7). For the face system, it was related to their trust on the system and if they have already tried before a biometric system (Fig. 6). Since most of the respondents (89.85%) were satisfied from the keystroke system and most of them (84%) were not disturbed while using it, their perception about the keystroke's performance was important. On the other hand, since 56.52% from the respondents have not already tried a biometric system and 33.33% from them do not trust face system, their perception about the face's performance was not important. These relationships explain why respondents found the keystroke's performance outperformed the face one; • Respondents' general appreciation on the face system was related to their trust on it (table IV). For the keystroke system, it was related to its performance and their trust on it (Fig. 7). We found that the trust factor was an important one that affects their general appreciation in both systems.

Since there was no significant difference of trust factor in both systems, we conclude that their perception about keystroke's performance was the main reason for which they were more satisfied in keystroke system than face one; Fig. 6. Excerpt from the bayesian network explaining respondents' answers of performance for face system Fig. 7. Excerpt from the bayesian network explaining respondents' answers of performance and general appreciation for keystroke system.

V. CONCLUSION AND PERSPECTIVES

A complementary evaluation methodology to performance evaluation is proposed in this paper. The proposed methodology focus on studying users' perception while using a biometric system to qualify its operationality and acceptability. It uses (i) Kruskall-Wallis test to determine if there is a significant relationship between demographic characteristics and users' perception and (ii) two data mining approaches, Bayesian networks and Decision Trees, that illustrate the dependencies between respondents' answers to explain their answers and behaviors. We have applied this methodology on 70 persons using two biometric systems based on physical (face verification, EER = 8.76%) and behavioral (keystroke dynamics, EER = 17.51%) analysis. The main results of the survey are:

• respondents considered that biometric-based technology is more appropriate than secret-based solutions against fraud; • demographic factors (age, gender and education level) have affected their answers on some perception questions; • both systems were acceptable and respondents were more satisfied with the keystroke system (89.85%) than the other one (81.16%). Trust factor has been identified as a major one that affects their general appreciation on both systems; • the robustness of the face system against attacks has been identified as an important factor that affects their concerns about privacy issues (46.4%); • Finally, from the volunteers that they have willingness to use the studied systems in the future, the keystroke system was more requested to be used to manage logical access and the other one for physical access.

Results presented in this paper show that users' perception is a crucial factor that we have to take into account when developing and evaluating biometric systems. Even if the performance of a biometric system outperformed another one, this will not necessarily mean that it will be more operational or acceptable. Robustness of a system against attacks and its perceived trust have been identified as important factors to take into account when designing biometric system. In our point of view, the main drawback of the widespread use of biometric technology is the lack of a generic evaluation methodology that evaluates biometric systems taking into account: performance, users' acceptance and satisfaction, data quality and security aspects. We intend to work on data quality and security aspects in the future.

Fig. 1 .

 1 Fig. 1. Evaluation aspects of biometric systems: performance, acceptability and satisfaction, data quality and security.

  rule 5: if no edges are directed then take a random one (first we can find).

Fig. 3 .

 3 Fig. 3. The two structures S 1 (left) and S 2 (right)

Fig. 4 .

 4 Fig. 4. Age and gender distribution of the volunteer crew

Fig. 5 .

 5 Fig. 5. DET curves for the two tested biometric systems

TABLE I PERCEPTION

 I QUESTIONS AND DEMOGRAPHIC FACTORS, KRUSKALL-WALLIS ANALYSIS: LINES WITH TWO P-VALUES CORRESPONDTO SYSTEMS' SPECIFIC QUESTIONS (FACE/KEYSTROKE)

	Perception questions	Gender	Age	Education	Profession
	Biometric technology knowledge	0.01	0.86	0.21	0.73
	Awareness about fraud identity	0.18	0.42	0.12	0.15
	Secret-based against fraud	0.18	0.6	0.008	0.61
	Biometric-based against fraud	0.19	0.32	0.09	0.54
	Disturbed	0.43/0.87	0.1/0.91	0.02/0.02	0.23/0.22
	Threats to privacy	0.51/0.89	0.95/0.76	0.19/0.006	0.7/0.23
	Easy to use	0.46/0.26	0.07/0.31	0.47/0.8	0.61/0.65
	Verification fast	0.2/0.14	0.42/0.85	0.26/0.16	0.13/0.26
	Correct answer	0.91/0.13	0.79/0.44	0.13/0.05	0.72/0.09
	System can be easily attacked	0.9/0.4	0.63/0.01	0.06/0.48	0.88/0.08
	Use in the future	0.14/0.14	0.46/0.98	0.02/0.74	0.38/0.19
	Trust	0.26/0.71	0.13/0.59	0.04/0.94	0.98/0.11
	General appreciation	0.07/0.12	0.2/0.56	0.26/0.52	0.07/0.58

  Romain Giot is a research engineer in the GREYC laboratory. He obtained his Master of Science in 2008 from ENSICAEN. His research interests biometrics, especially the definition of keystroke dynamics biometric systems and multibiometric systems. Baptiste Hemery is an assistant professor at IUT of Saint-Lo (France). He obtained his Ph.D. from the University of Caen Basse-Normandie in 2009. He belongs to the GREYC laboratory in the computer security research unit. His research interests concern image interpretation evaluation and biometric systems.Christophe Rosenberger is a Full Professor at ENSICAEN, France. He obtained his Master of Science in 1996 and its Ph.D. degree in 1999 from the University of Rennes I. He works at the GREYC laboratory. His research interests include computer security and biometrics. He is particularly interested in authentication methods for e-transactions applications. In your opinion, are secret-based solutions (eg. password) an appropriate solution against fraud (eg. e-commerce)? 11 . is it easy to use this system? not at all easy not easy easy quite easy I do not know Q 12 . Do you find the verification fast? not at all fast not fast fast quite fast I do not know Q[START_REF] Theofanos | Usability testing of height and angles of ten-print fingerprint capture[END_REF] . Is the answer of the biometric system is correct?

	APPENDIX
	SURVEY QUESTIONNAIRE
	Part A. Socio-demographic characteristics
	Date of birthday	. . .
	Gender	male female
	In which continent do you live?	asia europe north America
		south America other
	Highest education level	high school graduate	college
		graduate other
	Profession	student	worker	retired
		other
	Part B. General perception of biometric systems
	Q 1 . Have you ever heard before about	yes no
	biometric authentication systems (be-
	fore our study)?
	Q 2 . Have you ever tried a biometric	yes no
	system (before our study)?
	Q 3 . Have you ever been personally the	yes no
	victim of identity fraud?
	Q 4 . How would you rate your knowl-	not at all important	not im-
	edge about biometric technology?	portant	almost important	quite
		important I do not know
	Q 5 . How would you rate your awareness	not at all important	not im-
	about fraud identity?	portant	almost important	quite
		important I do not know
	Q 6 . strongly disagree	disagree
		agree	strongly agree	I do not
		know
	Q 7 . In your opinion, are biometric-based	strongly disagree	disagree
	solutions an appropriate solution against	agree	strongly agree	I do not
	fraud (eg. e-commerce)?	know
	Part C. Perception of the tested system
	Q 8 . Have you ever tried this biometric	yes no
	modality (before our study)?
	Q 9 . were you disturbed while using this	not at all disturbed not disturbed
	system?	disturbed quite disturbed I do
		not know
	Q 10 . does this technology threats your	not at all intrusive not intrusive
	privacy?	intrusive quite intrusive I do
		not know
	Q never		rarely	sometimes
		always I do not know
	Q 14 . In your opinion, is the system used	strongly disagree	disagree
	can be easily attacked?	agree	strongly agree	I do not
		know
	Q 15 . Are you ready to use this biometric	strongly disagree	disagree
	system in the future?	agree	strongly agree	I do not
		know
	Q 16 . If you are ready to use this system	physical logical
	in the future, would you like to use
	it for physical (eg. access a building)
	or logical (eg. log on to a computer)
	access?
	Q 17 . do you trust this system?	no at all	not really	rather
		yes I do not know
	Q 18 . What is your general appreciation	not at all satisfied	not satisfied
	of this system?	satisfied	quite satisfied	I do
		not know

ACKNOWLEDGMENT

The authors would like to thank the Basse-Normandie Region and the French Research Ministry for their financial support of this work.