HAVE'2007 - IEEE International Workshop onHaptic Audio Visual Environments and their ApplicationsOttawa, Canada 12-14 October 2007

Behavioral Features for Different Haptic-based Biometric Tasks

Rosa Iglesias1, Mauricio Orozco2, Julio J. Valdes3 and Abdulmotaleb El Saddik2Ikerlan Technological Research Centre

P°. J. Ma. Arizmendiarrieta, 220500 Arrasate-Mondrag6n, Gipuzkoa, Spain

riglesiasWikerlan.es2Multimedia Communications Research Laboratory - MCRLab

School of Information Technology and Engineering - University of OttawaOttawa, Ontario, KIN 6N5, Canada{morozco, abed}@mcrlab.uottawa.ca

3National Research Council of Canada, Institute for Information Technology, 1200Montreal Road, Ottawa ON KIA OR6, Canada

julio.valdesW nrc-cnrc.gc.ca

Abstract The science of haptics or haptic technology has gaining more interest due to the advantages of suchreceived enormous attention in the last decade for multiple systems over traditional authentication, to ensure that onlyapplications. A new promising example is the use of haptic- legitimate users can access the services. Biometricbased systems for individual authentication. A user 's behavioral systems identify users based on physiological orcharacteristics captured while interacting with a virtual scene . .are significantly more dfficult to compromise than traditional behairacharace stc [] Physiolgical biometricmeans (i.e. login ID and passwords). Moreover, another gsystems recognze a user by analyzing iris patterns,advantage is that this technology allows a user not only to gain fngerprints, faceiageorhand metry, ban ontheaccess to the system but also to continuously verify individual On the other hand, behavioral measures are based on theauthenticity during the whole session ofhaptic interaction. Our manner, for example, how people type in a keyboard,current haptic-based biometric system for authentication (the write their signature, dial a phone number, or the way theyBioHaptic system) has integrated a variety of applications: speak.solving a virtual maze, signing a virtual cheque and dialingphone codes on a virtual phone. In this paper, with the use ofa B. Haptics3D visual representation we study: (1) a subset offeatures withthe greatest user-classificatory worth and (2) whether or not The technology called haptics introduces the complexsuch features are dependent on the application used. We believethis study will enhance the success of individual authentication sense of touch into the human-computer interaction. Thisbased on human-haptic interactions. technology enables users to touch and manipulate virtual

objects in a bidirectional exchange of energy, whichKeywords - Authentication, Biometrics, Haptics, Virtual provides a true interface between human and machine.Reality, Visual Data Mining Moreover, with this technology, it is possible to measure

and record haptic data generated directly as users interact1. INTRODUCTION with the system. In that way, haptics can be seen as a

means to extract behavioral features that characterize abiometric profile for an identity authentication process.

A. Biometric Authentication The usage of haptics for authentication was motivatedby the idea that physical attributes, such as, velocity, the

Currently, almost all computer systems involve an orientation when holding the end-effector or stylus of theidentity authentication process before a user can access haptic device, or the force exerted during the hapticrequested services; such as, online transactions, logging sessions could be unique for each user.into a computer system, entrance to a secured vault,secure access to buildings, on-line gambling, and so on. C. Related WorkGenerally, traditional authentication and authorizationsystems are based on textual passwords and login IDs, Recently, within behavioral biometrics it has beenwhich can be easily compromised or 'hacked'. shown that identity recognition, based on human-haptic

Biometrics is the science concerned with measuring interactions, is feasible [3] [4]. The underlying assumptionbiological characteristics for identifying or verifying is that there are natural differences between theindividuals [1]. Recently, biometric systems have been psychomotor patterns exhibited by individuals in virtual

978-1-4244-1571-7/07/$25.OO ©2007 IEEE 102

HAVE'2007 - IEEE International Workshop onHaptic Audio Visual Environments and their ApplicationsOttawa, Canada 12-14 October 2007

domains when using haptic devices, assuming that each Figure 1 characterizes an architecture view of theindividual's haptic interaction is individualistic. Such an BioHaptic system. A user is able to work into a VE withassumption emerges from the analogue principle, which haptic feedback while the system is recording theassumes that it is very probable that almost everybody has behavioral performance of such a user. The haptic dataa unique way of writing a note, typing a message or generated are used to feed an authentication subsystem indriving a car. order to recognize the hand movement patterns described

The precision of a biometric system is reliant on users' by the original user. The precision of this biometricfeatures used to form their biometric profiles. In a haptic- system is reliant on the choice of behavioral featuresbased biometric system, behavioral features come from a (haptic data) used to form user biometric profiles. In thehigh number of attributes (i.e. time, position, force, Feature Generation/Selection subsystem, for accuratetorque, pressure and so on) which are sampled at a high identity recognition, features must be chosen so that theyrate (i.e. a few milliseconds) and its dynamics are are unique for each individual. In the Classifier Designmeasured over time. The relative entropy measure, or the subsystem, we employ pattern recognition methods toChi-squared test, provides us with a mechanism to authenticate the identity of these users. These methodsevaluate the user-classificatory worth of the different include k-means [7], Neural Network [8], Principalfeatures [6]. Although force and torque data seemed to Component Analysis (PCA) [9], Dynamic Time Warpingcontain the most information pertaining to identity, the (DTW) [10] or Fourier spectral analysis. In addition,amount of information in the features varied widely some applications, such as haptic-based rehabilitation,amongst the different users [6]. This variability poses a could use this system for patient diagnosis by analyzingproblem for the analysis precision of such haptic data. The haptic historical data. More details about the BioHapticanalysis of these data, with a high number of samples and system can be found in [11].with more than 3 dimensions, is very difficult to interpret. Three different haptic-biometric tasks have beenAnalyzing these data to compute the relative entropy or designed and tested (Figure 2). These tasks are based onusing the Chi-squared test and posterior analysis can be the well-known dynamic signature verification andvery tedious and time-consuming. keystroke dynamics [12]. These methods comprise the

The use of a 3D visual representation respecting the most important biometric systems based on behavioralinterrelationships, as defined by the original haptic data attributes.can help to analyze the information. Through thisrepresentation, different conclusions can be visuallydrawn, such as the user's variability on each trial and themost indicative of a user's features. ~t

2.T.E BIOHAPTIC SYSTEM

A Biometric Haptic system (BioHaptic) was designedand developed in our previous work [5]. This systemprovides the flexibility to construct a protocol for a) b)

1 * 11 *r~~~ ~ ~~~~~ ~ ~~~~~~~~~~~~~~~~-- - - ---------------------~--------------------------dynamically quantifying human hand psychomotor ------------------------

movement patterns during a user-haptic based interaction.

Intatwih E

extend f

,extend,,User ReodHpi C)

\Interaction F~=HuaHadClassifierDesign] -s___________

,,include,, Movement - )

incl ude,, Figure 2: Three applications in the BioHaptic system: (a) theextend, Biometric virtual cheque,(b)the virtual maze and (c)the virtual phone.

<<include,, ( Authentication )

Gain Access <c-e, The three applications were used with the ReachIn/ l; ~~~~~Feature system [13] - combining the single-point interaction

Generation/Selection/_ 7 ~~~Desktop PHANToM device [14] and stereo viewing.

Figure 1: An architecture view of the BioHaptic system

103

HAVE'2007 - IEEE International Workshop onHaptic Audio Visual Environments and their ApplicationsOttawa, Canada 12-14 October 2007

Virtual Cheque: This environment emulates users using the 3D visual representation are shown. In Section Cperforming a signature on paper. When signing the virtual all the haptic data captured for the three tasks arecheque, one can feel 'friction' between the stylus/end- considered for the analysis.effector of the haptic device and the cheque. As thecheque does exist in the physical domain, the friction A. Haptic Data Capturedexperienced by users is due to the force-feedback of thehaptic tool. The signature path is rendered while users are It is understood that the physical attributes capturedsigning. from haptic interfaces vary from device to device and its

Virtual Maze: this task involves the participant related software Application Programming Interfacecompleting a flat 2D maze. The haptic stimulus is (API). In our case throughout the Reachln system, theprovided by a function that emulates a given force following data were captured: timestamp, weightedresponse in order to prevent a user from passing through position, actual proxy position, actual device position,the walls of the maze. The sensation is as if the walls are force, torque and tracker angle or orientation (overall, 18"sticky". This application also graphically renders the attributes). For the user studied in this paper, the rawpen's maze trajectory using a line path (colored in light haptic data captured comprises around 15,297 data.blue).

Virtual Phone: This task allows users to virtually call B. Visualization ofthe VR-space Qsomeone. To make a phone call, the user must dial thenumber and then press the "green-phone" key. After a In our study, the VR-space Q is comprised of cubes,short interval of time, the subject then presses the "red- which are data objects/features. A class of objectsphone" key to terminate the call. represents a set of haptic data (captured on different days)

from the same participant for each of the haptic-biometric3. THE 3D VISUAL REPRESENTATION tasks. Each cube represents a set of objects that are

mapped to it from the original 15,297 objects. These dataGenerally, as was indicated earlier, the haptic data belong to the same user. On the other hand, each cube is

captured during an individual interaction are very large colored differently depending on the haptic-biometric(measured every few milliseconds) and have a high task. Moreover, some cubes can be substantially largernumber of attributes (position, velocity, force, angular than other objects, which indicate that a greater number oforientation of the end-effector and torque data, among objects, are represented. The bigger the cubes, the moreothers). Therefore, the behavioral haptic data that describe similarity. These cubes are also drawn with semi-users are defined in terms of a large number of features transparent faces for easy identification. A convex hull (awhich adds complexity to the analysis. It is desirable to wrapper around all of the cubes belonging to each classfind a 3D visual representation that shows the similarity of defined by a user) is drawn to more abstractly representa user's haptic data during different trials, as well as the the shape of the groups of objects (having particularexisting relationship among other users' features. The idea properties) in the constructed VR-space Q .is to construct a Virtual Reality (VR)-space to visualize In particular, it is also shown that the closer the cubes,interesting patterns or relationships, which can be the more similar the features. The location and adjacencysomehow hidden in the original data. This VR-space has relationships between the cubes in Q give an indicationbeen reported in [15] and keeps (within a certain about the similarity relationships of the participant amongthreshold) the similarity structure of the original data. The the tasks.VR-space is displayed by using a VRML model [16].

C. Allphysicalfeatures4. ANALYSIS

All the haptic data captured from the participant for theThis section evaluates the similarity of physical three tasks, is visually represented in Figure 3. Figure 3 a)

attributes or haptic data for the same user and for each of shows the cubes that represent each of the applicationsthe three haptic-biometric tasks. The participant signed and Figure 3 b) represents the convex hulls. In Figure 3the virtual cheque 9 times on different days, navigated the b), it is shown that the convex hulls appear as distantmaze 10 times and dialed familiar and non-familiar phone clouds occupying well-separated spaces (Figure 3b)) wasnumbers 10 times. Firstly, the haptic data captured while rotated for better visualization of the convex hulls). Thatthe participant was carrying out the tasks are described, means that the same user has some different physicalSecondly, the next section describes the main features of features depending on the application.the new VR-space to visually interpret the information.Finally, in the subsequent sections, the results obtained by

104

HAVE'2007 - IEEE International Workshop onHaptic Audio Visual Environments and their ApplicationsOttawa, Canada 12-14 October 2007

pressure for each application. The linearity showed (a linecould be drawn passing through all cubes means) that theuser's pressure seems to be relevant and less variantduring different trials for each application. Moreover, itcan be shown in the figure that the pressure applied whensigning and navigating a maze is different. The mostsurprising fact is that the pressure the user applied whendialing a phone number is similar to the pressure duringthe other two tasks, the maze and the phone tasks. Thepressure information for the virtual phone task iscontained in the cubes displayed in Figure 4.

a)-X - -_ - -_--_--_--_--_--_ -

|13 x ...-- , .........,I. |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.......

b) I

Figure 3: (a) 3D visual representation of three haptic-biometric Figure 4: Pressure applied for each applicationtasks for a user and (b) the corresponding convex hulls

The haptic data captured from the virtual phoneapplication appear very distant from the other twoapplications. This seems to be reasonable since the handposture when dialing phone numbers by means of a Lstylus/pen differ from the postures when signing ornavigating a maze. As expected, the haptic data from thevirtual cheque shows the most similarity (depicted as bigcubes in Figure 3a), seen an enlargement of the hapticdata pertaining to the virtual cheque task).

After studying the VR-space created and weightingdifferent features, such as, studying only the user'sposition, or the user's position and orientation altogetherand, so on. It was observed that pressure was very similar - -

among different trials (Section D) and pressure and torquecould also identify behavioral patterns for a user (Section Figure 5: Pressure and torque applied for each haptic-based taskE). for a user

D. Pressure E. Pressure and torqueIt has been shown that pressure can be an important

A single haptic measurement (i.e. force, torque, angular feature to characterize a user. On the other hand, torque inorientation and so on) does not characterize individual the x and y -axis seems to have potential for user-idntt.Thsi.u.ote .aiblt fth eut o h classificatory worth. Figure 5 represents the VR spacesaeuer mn dfeen ras.Hwve,frintne when only pressure and torque (x and y components) arewhewrtn onapprtepesr'euewe considered as haptic data captured for each application. Itprsigtepnaantteseto*ae sasmdt can be observed that Figure 3a) and Figure 5 are very

be soewhatindictive f a uer. Fgure show the similar (Figure 5 is rotated with respect to Figure 3a). Thiscould mean that among different samples the features that

105

HAVE'2007 - IEEE International Workshop onHaptic Audio Visual Environments and their ApplicationsOttawa, Canada 12-14 October 2007

remain somewhat stable or less variant are pressure and [2] Wayman, J.L.: Fundamentals of Biometric Authenticationtorque. Therefore, these features seem to have great user- Technologies. Proc. Card Tech/Secure Tech, 1999.http://www.engr. sjsu.edu/biometrics/nbtccw.pdfclassificatory worth. [3] Orozco, M., Shakra, I., and El Saddik, A.: Haptic: the new

biometrics-embedded media to recognizing and quantifying5. CONCLUSIONS AND FUTURE WORK human patterns. In Proceedings of the 13th Annual ACM

international Conference on Multimedia (Hilton, Singapore,November 06 - 11, 2005). MULTIMEDIA '05. ACM Press, New

As haptic users interact in a virtual environment, York, NY, 387-390.multiple haptic data, such as, time, position, force, torque, [4] Orozco, M., Asfaw, Y., Shirmohammadi, S. S., Adler, A., andpressure, among others, can be measured and recorded for Saddik, A.: Haptic-Based Biometrics: A Feasibility Study. Inauthentication purposes. For successful authentication, it Proceedings of the IEEE Virtual Reality Conference (VR 2006) -is desirable to find the physical attributes (haptic data) that [5] M.Orozco , IShakra and AuEI Saddik, "Adaptive Hapticcharacterize a user, that is, assess relevant attributes with Framework", In proceedings of the IEEE International Conferencethe greatest user-classificatory worth. on Virtual Environments, Human-Computer Interfaces, and

In this paper, different haptic data for the same user Measurement Systems, Giardini Naxos, Italy, 18-20 July 2005.recorded through the following actions: signing a [6] A. El Saddik, M.Orozco, Y.Asfaw, S.Shirmohammadi and A. Adler,was "A Novel Biometric System for Identification and Verification of

virtual cheque, navigating a virtual maze or dialing phone Haptic Users", IEEE Transactions on Instrumentation andnumbers in a virtual phone. The role of a 3D visual Measurement to accepted to appear 2007representation for the understanding of these haptic data [7] K-Means Clustering Method

http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial htmlwas crucial. The visual representations have aided in the /mashm/kmeans.htmldevelopment of certain conclusions. Since the shape of the [8] C. M. Bishop (1995). Neural Networks for Pattern Recognition.convex hulls containing haptic data appeared somewhat Oxford University Press, Inc.compact and oriented, it can be interpreted as if the haptic [9] 'Principal Components and Factor Analysis':data had certain uniqueness for each user and for each http://www. statsoft. com/textbook/stfacan.html

[10] J. E. Keogh and M. J. Pazzani, "Derivative Dynamic Timetask. On the other hand, as expected the haptic data Warping," Department of Information and Computer Sciencecorresponding to the signature shows the less variability University of California, Irvine California 2000.with respect to the other tasks. The pressure that the user [1 1] R. Iglesias, M. Orozco and A. El Saddik. Haptics for Recognizingapplied when navigating the maze was different to the and Quantifying Hand Movement Patterns for Authentication.pressure when signing. Furthermore, boh pressurettnd WISP 2007, October 2007.

pressure when signing. Furthermore, both pressure and [12] C. Vielhauer Biometric user authentication for IT security: fromtorque (on the xy-plane) seems to have great user- fundamentals to handwriting. Springer, New York, 2006classificatory worth. [13] Reachin Technologies: http://www.reachin.se/

As per future work, further analysis will be performed [14] Sensable Technologies, The Phantom:wit more users.Moreover, more physical features to [15] http://www.sensable.com/haptics/products/phantom.html.with more users. Moreover, more physical features to [15] J.J. Valdes and G.F. Bonham-Carter. Virtual reality representation

assessing the unique-to-the-individual behavioral of geoscience databases and decision making knowledge.attributes will be studied. [16] http://www.web3d.org/

REFERENCES

[1] Bolle, R. M., Conell, J. H., Pankanti, S., Ratha, N. K., and Senior,A.W.: Guide to Biometrics, Springer Professional Computing,Springer, New York 2004.

106