DRIVER AUTHENTICATION USING BRAIN WAVES WHILE

ROUTE TRACING AS A MENTAL TASK

Isao Nakanishi

Baba Sadanao

and Masahiro Inoue

Graduate School of Engineering, Tottori University, 4-101 Koyama-minami, Tottori, Japan

Faculty of Engineering, Tottori University, 4-101 Koyama-minami, Tottori, Japan

Keywords:

Biometrics, Driver Authentication, Brain Wave, Route Trace, Mental Task.

Abstract:

From the viewpoint of user management, continuous or on-demand biometric authentication is effective for

achieving higher security. In such a case, the biometrics which is able to present biometric data unconsciously

is needed and we have proposed to use the brain wave as the unconscious biometrics. In this paper, assuming

driver authentication, we measure brain waves of drivers when they are tracing routes as a mental task. And

we evaluate veriﬁcation performance using the difference between the mean power spectrum at α-β band in

relaxed condition and that in mental-tasked condition as an individual feature. As a result, the EER of 31 % is

obtained among 12 subjects.

1 INTRODUCTION

For secure networked society, person authentication

using biometrics gains public attention. Among bio-

metric traits, the ﬁngerprint and the iris achievehigher

performance and are already used in consumer secu-

rity systems. However, it has been reported that au-

thentication systems using them were circumvented

by using fake ﬁngers or printed iris images (Mat-

sumoto et al., 2002; Matsumoto et al., 2007). The

reason is that the ﬁngerprint and iris are revealed on

body surfaces. The vein is kept in the body; therefore,

it is expected to have tolerability to the circumvention.

However, it is also reported that even the authentica-

tion system using the vein accepted artifacts in enroll-

ment and veriﬁcation (Matsumoto, 2006). This is due

to lack of the function of liveness detection which ex-

amines whether an object is a part of a living body.

The liveness detection scheme is necessary for pro-

tecting biometric authentication systems from spoof-

ing using artifacts.

On the other hand, conventional biometric sys-

tems mainly assume applications based on one-time-

only authentication such as access control, banking,

passport control, and so on. However, from the view-

point of user management, the one-time-only authen-

tication is low-security. After authenticating by a gen-

uine user, even if he/she is switched to an imposter,

the one-time-onlyauthentication system could not de-

tect such spooﬁng.

Figure 1: Styles of authentication.

Figure 1 shows conceivable styles of authentica-

tion where (a) is the one-time-only authentication.

Assuming that both authentication and application ex-

ecution are simultaneously achieved in a single sys-

tem, the one-time-only authentication is not a heavy

load since the authentication is achieved only once at

the start of the application while security is not guar-

anteed after the authentication.

Nakanishi I., Baba S. and Inoue M..

DRIVER AUTHENTICATION USING BRAIN WAVES WHILE ROUTE TRACING AS A MENTAL TASK.

DOI: 10.5220/0003463900900096

In Proceedings of the International Conference on Security and Cryptography (SECRYPT-2011), pages 90-96

ISBN: 978-989-8425-71-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

In order to cope with this problem, continuous

authentication is proposed (Altinok and Turk, 2003;

Kwang et al., 2009), which is shown as (b) in Fig. 1.

The security is guaranteed all the while that the appli-

cation is executed but the continuous authentication

brings a heavy load for the system. In fact, it was

reported that overhead of 42% was yielded in a con-

tinuous authentication system (Kwang et al., 2009).

On the other hand, we have proposed on-demand

authentication, where users are authenticated on a

regular or nonregular schedule on demand of authen-

tication from the system (Nakanishi et al., 2010). It is

illustrated as (c) in Fig. 1 and makes the system load

lighter.

By the way, the ﬁngerprint and the iris are not suit-

able for the on-demand authentication because they

ask users to present their biometric data every au-

thenticating. In other words, the continuous or on-

demand authentication needs unconscious biometrics.

As the unconscious biometrics, the face, ear, voice,

keystroke and gate are applicable but the face and the

ear are easily imitated using artifacts and the voice,

keystroke and gate limit applications.

It has been proposed to use the brain wave as the

biometrics (Poulos et al., 1999c; Poulos et al., 1999a;

Poulos et al., 1999b; Paranjape et al., 2001; Ravi and

Palaniappan, 2005; Palaniappan, 2005b; Mohammadi

et al., 2006; Marcel and Millan, 2007; Palaniappan

and Mandic, 2007; Singhal and Ramkumar, 2007; Ri-

era et al., 2008). The brain wave is generated by ac-

tivities of neurons in the cerebral cortex; therefore,

it is kept in the body and so it is effective for anti-

circumvention. Of course, the brain wave possesses

the function of liveness detection because it is gener-

ated only by live human beings. Moreover, the brain

wave is generated autonomously and unconsciously;

therefore, it enables the on-demand authentication.

Conversely, since users are required to put sensors on

their scalp every authentication under present tech-

nologies, the brain wave is not suitable for the one-

time-only authentication

Considering these facts, operator veriﬁcation of

systems such as computers and vehicles are suitable

for the authentication using the brain wave as shown

in Fig. 2. The operator wears a brain wave sensor

and he/she is veriﬁed on demand while using the sys-

tem. For instance, in remote education systems, stu-

dents should be authenticated while learning. It is

even more so for the students who are trying to obtain

some academic degree or public qualiﬁcation. Also,

operators of public transportation systems should be

authenticated while operating since hundreds of hu-

It will be solved if contactless sensors for detecting

brain waves are invented in the future.

Figure 2: Authentication of operators using the brain wave.

man lives depend on them. There are other examples:

pilots of aircrafts, drivers of emergency vehicles, op-

erators of military weapons and so on.

We had conﬁrmed veriﬁcation performance using

the α band in the case where users were relaxed in

eye-closed condition (Nakanishi et al., 2009). Also,

assuming user authentication of computers, we eval-

uated the veriﬁcation performance when users were

concentrating on the mental composition with closed

eyes (Nakanishi et al., 2010). The mental composi-

tion was a mental task in which the user was mentally

making sentences. However, these were in eye-closed

condition. For applying the authentication using the

brain wave in practical environments, it is needed to

evaluate veriﬁcation performance in eye-opened con-

dition.

In this paper, assuming driver authentication,

users (drivers) are authenticated by using brain waves

while virtual-driving. As a mental task in eye-opened

condition, we adopt route tracing in which the drivers

verify actual routes with memorized ones.

If the detection of catnapped and/or drunkard op-

erators using the brain wave is possible, it is ex-

pected to be integrated with the operator’s on-demand

authentication and will become valuable protection

against having accidents.

2 VERIFICATION USING BRAIN

WAVES

2.1 Brain Wave

Electrical changes from large number of synapses

(neurons) in the cerebral cortex are accumulated and

then detected as a brain wave (Electroencephalogram:

EEG) on scalp using an electrode. Because of spa-

tiotemporal dispersiveness of neurons, there are not

distinct patterns in the EEG in general. However,

when the activity of the cerebral cortex becomes

low, brain waves partially become synchronous and

thereby some distinctive wave is observed. As such

waves, δ (0.5-3Hz), θ (4-7Hz), α (8-13Hz), and β

(14-30Hz) are well known and detectable when hu-

DRIVER AUTHENTICATION USING BRAIN WAVES WHILE ROUTE TRACING AS A MENTAL TASK

Figure 3: Block diagram of the proposed veriﬁcation system.

man beings are during deep sleep, getting sleepy, re-

laxed with closed eyes, and in some mental activity,

respectively. In particular, the α and/or β waves are

applicable for person authentication.

2.2 Feature Extraction and Veriﬁcation

In order to actualize the authentication using the

EEG, architecture for feature extraction and veriﬁca-

tion must be as simple as possible.

We have conﬁrmed that the spectral distribution

in the α band is an important feature for distinguish-

ing individuals (Nakanishi et al., 2009). It is, how-

ever, known that when some mental activity is being

done, the α wave is suppressed while the β wave be-

comes detectable. Also, for veriﬁcation in mental-

tasked condition, we proposed to fuse an individual

feature in the β band with those in the α band (Nakan-

ishi et al., 2010).

In this paper, dividing α-β band into several parti-

tions, the difference between a mean value of power

spectra in tasked condition and that in relaxed con-

dition at each partition are utilized as an individual

feature.

The reason why the α-β band is divided is that

the distribution of spectra in the α-β band depends

on an individual; therefore, each partition has differ-

ent effect on veriﬁcation Distribution of spectra at α-β

band is not a uniform state. Therefore, to ﬁnd and uti-

lize distinguishable partitions might be effective for

veriﬁcation.

The block diagram of the proposed veriﬁcation

system is described in Fig. 3. In advance of the veriﬁ-

cation, the enrollment of templates is performed. The

enrollment has two stages. Firstly, EEGs of a user in

relaxed and eye-opened condition are measured sev-

eral (L) times and then an ensemble mean value of L

power spectra in each partition is calculated. Next,

in mental-tasked condition, similar measurements are

done and then each ensemble mean value of power

spectra is obtained. The mean values in all partitions

are stored as templates in the system.

In the veriﬁcation, an EEG is measured once in

the mental-tasked condition and the veriﬁcation score

S is calculated by

S =

∑

p=1

||s

− s

| − |s

− s

(1)

where P is the number of partitions, s

and s

are

templates in the relaxed and tasked condition, respec-

tively and s

is a mean value of a partition in the

tasked condition at the veriﬁcation stage.

If S is less than a threshold, the user is authenti-

cated genuine.

SECRYPT 2011 - International Conference on Security and Cryptography

3 DRIVER AUTHENTICATION

WHILE ROUTE TRACING

Several authentication methods using the EEG in

mental activities have been proposed (Palaniappan,

2005b; Marcel and Millan, 2007; Palaniappan,

2005a). The mental tasks are, however, invented

from a viewpoint of brain sciences: mental arithmetic,

mental rotation of a three-dimensional block and so

on. In the case of on-demand authentication, if actual

tasks (works) are different from mental ones, users

are required to perform the mental task every authen-

tication and thereby it makes the authentication con-

scious. The mental task should be related with the ac-

tual one for keeping the authentication unconscious.

In the authentication of computer users, we mea-

sured EEGs during a mental task where subjects are

making sentences mentally (Nakanishi et al., 2010).

This task was called the mental composition. In this

paper, we assume authentication of drivers and mea-

sure EEGs when subjects are driving.

3.1 Route Trace

Drivers control their cars by processing audio-visual

information and then moving four limbs. Addition-

ally, the information processing and the motion are

repeated in no time; therefore, it is difﬁcult to extract

only the mental task in practical driving. On the other

hand, it carries a risk of having some accident in ex-

periments for subjects to drive cars actually. As a re-

sult, it is necessary to deﬁne what a mental task is with

risk-free in driving.

In this paper, we deﬁne a new mental task where

drivers are checking landmarks in view with their

memorized ones. For convenience, we call this task

route trace hereafter. The route trace is a supposable

task for the drivers; therefore, it enables unconscious

authentication.

3.2 Making of Driving Scenes

In order to perform the route trace in a laboratory en-

vironment, we prepared driving scenes which were

presented to subjects. They were captured by a digi-

tal video camera which was put on a dashboard in the

forward direction of a car. A still image of the scene

is indicated in Fig. 4.

The car was driven to turn at as many intersections

as possible in order to givethe subjects a heavy mental

load in verifying landmarks in view with their memo-

rized ones. Captured scenes are loaded in a computer

and scenes with poor visibility and/or out-of-focus are

Figure 4: A driving scene.

eliminated and as a result, we prepareda drivingscene

of three minutes long.

3.3 Measurement of Brain Waves

The number of subjects was 12. All are male around

20 years old and had driver’s licenses. Firstly, the

driving scene was presented to them while they were

requested to memorize landmarks. Next, a brain wave

sensor was worn on them and the same driving scene

was presented again. The subjects were requested to

trace a route by checking the landmarks.

The brain wave sensor was a consumer single-

channel electroencephalograph. By using a headband,

a single electrode (sensor) was set on the frontal re-

gion of head which corresponded to the frontal pole

(Fp1) deﬁned by the international standard: 10/20

method. The speciﬁcations are summarized in Table

Table 1: Speciﬁcations of the brain wave sensor.

Frequency Range 1-24 Hz

Minimum Voltage 5 µV

p−p

Maximum Voltage 80 µV

p−p

Sampling Frequency 128 Hz

An experimental scene is shown in Fig. 5. Distance

between the subjects and a display was about 1 m.

The measurement was carried out twice a day and

it was repeated ﬁve days and so 10 EEGs were ob-

tained from each subject and 120 EEGs were obtained

in total. In addition, for making templates, ﬁve EEGs

in relaxed condition for each subject were obtained by

measuring once a day for ﬁve days.

DRIVER AUTHENTICATION USING BRAIN WAVES WHILE ROUTE TRACING AS A MENTAL TASK

Figure 5: An experimental scene.

3.4 Veriﬁcation Experiments

EEGs of one minute long were used in veriﬁcation.

The average number L was set to ﬁve, that is, ﬁve

EEGs of each subject were used for generating his/her

templates. The rest ﬁvedata of each subject were used

for the veriﬁcation and all other subjects’ data were

used as those of imposters.

For reference, spectra of two subjects at α −

β band in relaxed and mental-tasked condition are

shown in 6. Additionally, frequencies which caused

these variations depend on the subjects; therefore, it is

expected that the difference between the power spec-

tra in relaxed and in mental-tasked condition becomes

an individual feature.

In this paper, setting the number of partitions in α-

β band: P to 4, that is, 8-11 Hz, 11-14 Hz, 14-17 Hz

and 17-20 Hz, we evaluated veriﬁcation performance

at each partition by using the equal error rate (EER)

where the false acceptance rate (FAR) is equal with

the false rejection rate (FRR). The results are summa-

rized in Table 2. It is interesting that each band has

Table 2: Veriﬁcation performance.

Partition (Hz) EER (%)

8-11 41

11-14 37

14-17 32

17-20 34

a different EER. In particular, the EER of 8-11 Hz is

the biggest. As shown in Fig. 6, the discriminative

spectra in α band in the relaxed condition were not

observed in the mental-tasked condition. Such a large

variation might have an inﬂuence on increasing the er-

ror rate since normalization was not performed in this

veriﬁcation.

Next, we evaluated the performance by combin-

(a) Subject A

(b) Subject B

Figure 6: Spectra of two subjects, in (a): relaxed condition

and (b): mental-tasked condition.

ing scores in three partitions except the 8-11 Hz. The

variation of the FAR and FRR with the threshold in

the ﬁnal decision are plotted by the solid line and the

dashed one, respectively in Fig. 7. The intersection

of these curves corresponds to the EER and it was 31

% in this case.

SECRYPT 2011 - International Conference on Security and Cryptography

Figure 7: Error rate curves.

4 CONCLUSIONS

Assuming driver authentication as one of applications

based on on-demandoperator authentication,we mea-

sured EEGs of drivers when they were route-tracing.

And we evaluated veriﬁcation performance using the

difference between the power spectrum at α-β band in

relaxed condition and that in mental-tasked condition

as an individual feature. Using 12 subjects, we ob-

tained the best EER of 31 % when the scores in 11-14

Hz and 14-17 Hz are combined.

However, the performance is not high enough to

conclude that the authentication using the brain wave

is ready for practical use. There are many problems

to be overcome. We are now assembling the database

of the brain wave using large number of subjects and

evaluating the veriﬁcation performance. It is also a

problem to introduce more powerful method into ver-

iﬁcation. In the future, it is necessary to evaluate

not only veriﬁcation performance but also usability in

the on-demand authentication system using the brain

wave.

ACKNOWLEDGEMENTS

A part of this work was supported by the Support

Center for Advanced Telecommunications Technol-

ogy Research, Foundation (SCAT) in Japan.

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SECRYPT 2011 - International Conference on Security and Cryptography